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dabatcavebyhonie · 5 years ago

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Representation In Media

From things that have happened and from things as they exist and from all things that you know and all those you cannot know, you make something through your invention that is not a representation but a whole new thing truer than anything true and alive, and you make it alive, and if you make it well enough, you give it immortality. -Ernest Hemingway

When growing up I barely found many role models who talked like me, looked like me, or acted like me. I didn't see through representation until 6th grade. Granted that's better than many women, but they didn't come from your regular characters. The characters that had the most impact on me were cartoons. Cartoons like Fillmore, Total Drama, sixteen, and other numerous shows that turned me into the person I currently am: A headstrong goth, geeky, black, female. The showed representation of the role-models I wanted to become and who I am. I also started to understand how these groups in the media are portrayed.

BEING A BLACK FEMALE

One of the most interesting things I have observed (In my short period of life) is the change of how black women are shown in media. I remember learning in elementary school that my Afro or my braids were seen as messy or not seen as beautiful and so for a while I would resolve to straighten my hair. Then, in 7th grade, the way my natural hair was seen, changed. People started to see my coils and braids as desirable. My conclusion at this age was that famous celebrities started to wear their hair with pride. And how non-black or people not of color would wear it as a trend. Controversial but, that's what I saw as a young girl of color. I was confused because I was used to not seeing my hair in the media or girls with curvy builds. I wonder during that time period what did others see? I started to see shows with black supporting characters wearing curls. So what happened? When did my hair in mass media become a trend? This question and more became frequent in my middle school years. Of course, it wasn't like I never saw people who looked like me in media. I saw Beyonce, Tiffany Haddish, Nikki Minaj, and even Michelle Obama; but even though I saw them in real life how I saw them in movies, and social media I saw arguments from both sides. For example, In 5th grade, I had a project I had two people I wanted to be Mae C. Jemison and Michelle Obama. I chose Michelle because of how powerful and smart she is and how beautiful she was. However, I was surprised when I heard people called her "ugly" or how she "looked" like a man. I was distraught and hurt to hear my classmates and their parents saying such rude things about my role-model. I was distraught. I was used to hearing people in my community saying only good things about Mrs.Obama. I decided to let this go because I knew how I saw her. But, I wanted to know why people said that, after all, I never heard anyone saying such things about Jackie Kennedy or any first lady previously. This question of how black people are perceived in the media re-surfaced later in 8th grade. I was having a conversation in the car with my aunt. She asked when in movies how are black women seen in movies (when they aren't made to pander to the black community). Her friend said they are seen as loud, talkative, and hot-tempered. I thought about this. When I saw a movie with a black female they were always seen as the best friend. They were shown as smart; I saw them as the classic black girl stereotype. They tended to not be in high paying jobs, they shouted, they would curse, they would be lustful. Not, that there is anything wrong with this arch type in media they tend to add personality to a show or film. However, I rarely saw a black female in film or TV often stray from this. And shows kids in my generations see as revolutionary like That's So Raven would also fall back to this character type. Me being the nerd I am I had to also analyze cartoons as well. However, this is when my search became foggy and convoluted. Black female characters in cartoons were rare. This started a whole new discussion.

BLACK FEMALES IN CARTOONS

Hi, I am a cartoon addict. I admit it. Whether they be Japanese, French, Canadian, or American I love them. But, I rarely see myself in them. Even if Black female characters exist in a cartoon show, they are often voiced by the same person: Cree Summer. Look at your favorite shows with a black female: Rugrats, Bratz, Code name: Kids Next Door, Danny Phantom, The Proud Family, and etc. Look in the imdb I posted below.

It makes me think how rare they are. And I don't recall many shows with one of the lead characters being a black female. Maybe Keesha from Magic School bus, Valerie from Josie and the Pussycats, Susie Carmichael from Rugrats, Shana from Jem, Storm from X-men, or any black female in The Proud Family. Makes you think.

These are characters are so rare and often aren't seen in every episode. So to see a female of color in a show makes me want to watch just to see someone who looks like me. Which is why I ended up getting into the show Wakfu. Amalia is a woman of color and a princess. For anyone who likes adventure cartoons with a hero's quest I highly recommend Wakfu (watch it in the original language: French. It's so much better than any of the dubs). Amalia is a princess and a prominent leader in her kingdom: Sadida. Not only that she fights in battle with her friends and is a lead character. She's a great role model but, I didn't watch this as a younger kid but, a high schooler. Isn't it important as a child to see yourself in the shows you watch? This is why a character like Garnet is so important! Even if she is an alien, she is presented as a woman of color with an Afro. To see yourself in a show gives self-worth, confidence, and self-identity. SELF IDENTITY IN CHARACTERS

As I said earlier cartoons helped shape me as a person. I loved shows Like The Addams Family, the Munsters, and Sabrina The Teenage Witch. I do say cartoon characters like Raven from Teen Titans, Penny from the Proud Family, Nikki from Sixteen helped make the person I am. These characters ended up being the people I related to the most because they had the persona I was and still am. I am an edgy teen who loves candelabras like Raven. I am a try hard like Penny. And I sound like Nikki vocal and speech pattern-wise. These cartoon characters were who I ended up identified with not my role-models, not the stereotypical characters in movies or TV. I liked cartoons, I liked Sam from Danny Phantom because she was funny, caring, and headstrong. As kids grow up (including me now) we must find people we want to be who we connect to. They don’t need to be found in a history book they can be a man-made creation like a cartoon. A cartoon was made for somebody, so enjoy them. If you find someone you identify with it will tell you more about yourself then you may realize. For example how I entered the Gothic community was through TV and literature and then I researched stumbled upon the music this sub-culture loves and I fell in love with it. It started with Morticia Addams Sam, Gwen, and Raven; this ended with me becoming what I loved.

CONCLUSION

So, the importance of variety in media is important. Not just cartoons; you should want to see yourself in media and there is nothing wrong with identifying with characters. It’s okay to want to be represented in media. A show like The Boondocks is revolutionary, Michiko & Hatchin are important, shows like American Dragon are important, movies like Crazy Rich Asians are important because they represent YOU. They are society. Not everyone is a mainstream, heterosexual, cis-gendered, male (If you are that’s pretty neat!), so the representation of YOU is important in media. I’m glad people love my hair and I see it in films and other mass media. I am happy I see bisexuals in TV shows like because I feel less stigmatized when I see bisexual on the screen( if you didn’t know Eleanor is a bisexual). I am happy to see me in the media and I am happy to see YOU in the media. Representation in the media is important.

#wakfu amalia #jem and the holograms #the addams family #the boondocks #"Michiko and Hatchin

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luxmagnafest · 5 years ago

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INTERVIEW: "Reflectif" Artists Reflect on Black Representation from their Upbringing

In one week, Lux Magna will have the pleasure of opening a month long art exhibit at Casa del Popolo, curated by local visual artists Kai Samuels and Joyce Joseph (a.k.a. JUICE); Reflectif is an exposition of art spanning various mediums, by 6 young Black artists coming from across the country.

Team member Mags (who is also a visual artist) spoke with Nafleri, Tyrin Kelly, Joseph Moore, Hasina Kamanzi (OTT), BlazenBlack (OTT) and Simone Heath (TO), about their respective experiences growing up with (or without) Black folk represented in the media and art that they consumed.

When was the first time you remember seeing Black folk represented in media or the arts?

Hasina: The first time I remember seeing Black people highlighted in media was when I went back to Burundi for the first time in 2014. I saw an oil painting exposition that was illustrating what life was like in Burundi pre-colonisation. I didn't realize at the time how influential it would be for me so, unfortunately, I can't recall what was the name of the artist or the name of the exposition.

Nafleri: Having grown up in Haiti, I was surrounded by Black people, so Carnival season was Black people and their joy put on a show. I knew whiteness existed but it was in light-skin Black [people] or missionaries; I wasn't fully aware how much opportunities catered to it. BUT, after arriving in Canada and being taught to be Black, around my second year, I remember TVA played films every Saturday, and during the week they would play the trailer for said movies; I remember once they played Fat Albert and all through out the week I was hype ‘cause it was movies with characters I felt I could relate to. I ended up being disappointed but, I still remember that child's hype. But in Haiti, I remember music, cinema, literature, paintings, sculptures, I wasn’t fully aware of it but I was lucky enough to experience Blackness in art.

Joseph: The first time that I remember seeing Black people represented within the media was The Proud Family. The show had a significant impact on my childhood, as it allowed for me to see various Black characters in a normalized and lighthearted setting on a regular basis.

BlazenBlack: Had to be the detective [Bulletproof] in the cartoon COPS, followed by X-Men’s Storm.

Simone: The earliest Black character I could remember is Susie Carmichael from Rugrats. Pinpointing a first time is hard to say for sure. I grew up in the late 90’s-early 2000’s with a lot of Black shows, a few having more Black-centric protagonists. I can remember watching The Fresh Prince of Bel-Air, Family Matters and The Cosby Show with my family.

Tyrin: I’m not sure… growing up I became really obsessed with the early jazz scene in America. It was drummers like Philly Joe Jones and Art Blakey that really inspired me to learn an instrument. K-OS is one of the first modern Black musicians that showed me you can make hip-hop and be a rock-star. I fell in love with Atlantis Hymns for Disco and really idolized that whole “B-Boy who makes indie music” persona. In terms of visual art I wasn’t really aware of Black artists that stuck to my memory until high school art class I think. I was really invested in the poetry scene in Ottawa during that time and Saul Williams is another Black artist that really influenced me.

2. Who was your favorite fictional Black character growing up?

Nafleri: Can I answer Jesus? (laughs) I remember reading (I know nada of Christian theology) that Jesus never wrote anything, his partners did, so in the writing of others, I'd see the fiction of Jesus, not that it's a bad thing, fictional characters can be inspirational but… uncles, aunties and ‘em might roast for that one. (laughs). Jokes aside though, growing up I remember Bouki and Malice, which were folk stories of Haiti and in the literary work of Odette Roy Fombrun. I was able to see Black characters that weren't asked to be super, they existed in the complexities of their life. Looking back, I'm grateful to have experienced that.

BlazenBlack: My favorite fictional Black character must have been Piccolo [DragonBall Z] if he counts. If not, War Machine [Iron Man franchise].

Hasina: Growing up, my favorite Black fictional character was Pamela (from the Tea Sisters book series) [Thea Stilton series in North America]. Technically, she's a mouse but she was also very anthropomorphic & born in Tanzania (like me!) so baby Hasina read her as Black.

Joseph: This is a hard question to answer as I can think of many favourites, but if I had to choose, it would be between Alyx Vance from the Half Life video game series or Michonne from The Walking Dead.

Tyrin: My favourite fictional Black character growing up was Radio Raheem from Do The Right Thing. Also Q from Juice. Foxy Brown was also so badass. Those three will forever be cool.

Simone: Probably Raven Baxter from That’s So Raven for a bit. I liked a lot of the outfits she would wear. She was multi-talented and funny.

3. What is your opinion on the current state of Black representation in Canadian media?

BlazenBlack: I don’t watch much Canadian TV, so I can’t speak on shows or movies, but in terms of animations, I can’t even name one off the top of my head. I'm hoping to change that.

Joseph: While I admit that I haven't been consuming as much Canadian media as I would like to as of late, I have found it harder to name many prominent or relatable Black characters within Canadian media off of the top of my head as opposed to American characters. While I appreciate Canada's willingness to represent many different cultures and viewpoints, it would be interesting to see something centered around the regular lives of Black people living in Canada on a larger platform.

Simone: Black representation in Canadian media could be a lot better. It feels as though it isn’t really there or pushed into the background as apart of Canadian diversity. Most of the Black media I consume is from the States. I don’t watch a lot of Canadian television, but from what I’ve seen I don’t recall any Black protagonists, usually side characters with little to no background. I feel like Black Canadian artists/athletes aren’t recognized until they have made something of themselves outside of the country. I’m grateful for people that reach out and organize events like this to have ourselves shown. I also have a lot to learn myself when it comes to being more active in these conversations and connecting with other Black Canadians.

Nafleri: I feel like I can't speak of Canadian media, though this stretch ocean to ocean I've only visited— I can't even say Toronto— Niagara Falls… once on a family trip. Quebec media however, having consumed a lot, hoping to fit in, I know for a fact, there is a big lack of representation. Though I stopped consuming QC media, late high school, my best friend studying in a theater institutions is closer to Quebec's media and we often discuss the lack of representation in his future field of work.

Tyrin: Um, I’m not sure it’s so black and white… if you’re looking at “credible” sources of media, yeah definitely a little convoluted. But in terms of independent media— media environments run by artists for artists— then I think it’s thriving and it’s all so cool! Like, looking at people I follow on social media or friends and peers that are making cool shit the list is giant. Definitely media representation is positive and important to an extent, and I think in Canadian media the effort is made, but that’s not what matters. What matters is honesty and published honesty is recognized in every format. I mean, shouts out to: Tau Lewis, Marvin Luvualu António, Moneyphone, Schwey, Elle Barbara, Tati au Miel, Neo Edo, Cole Craib, James Goddard, and all other Black artists who are doing their thing.

Hasina: There is a lot of work to be done but I'm hopeful because I see a lot of creatives doing great things both in Ottawa/Gatineau (where I live) and in Montreal.

Closing thoughts from co-curator JUICE

It's really cool how living Black is very different to other people. I always had this ideology that, because I was navigating spaces where there were a few Black children (or I was the only Black child) while growing up, meeting Black folks outside my environment meant that I could relate to them, just because they were Black. I wouldn't realize that our experiences could be different. Seeing how representation is so different but so important to each individual life, reminds me that, what ever they're doing creatively, you can do too, and you're not alone on the journey.

The first time I remember seeing Black folks was when my mother gave me a Spice Girl doll. Mel B (Melanie Brown) was the first Black doll I ever had and she had an Afro. She was the one doll I spent so much time on; I loved her so much. When I found out she was an actual person, I was shocked, and was interested in what she does, but I didn't have access to seeing what she did creatively (except on those celebrity TV talk shows when my mother would take me out to hair salons) . Later on growing up, I was into 1990's-2000's TV shows. The Fresh Prince of Bel-Air, My Wife and Kids, and Sister, Sister to name a few. Cartoons and anime were some other things I would watch when I would spend time with my siblings. Codename: Kids Next Door, Teen Titans and Bleach were a few of the earliest shows I would see Black characters. I would be extremely happy whenever they appeared on my screen. This only lasted during the years I moved and lived in Philadelphia. I moved back to Canada in 2008 and my spaces drastically changed. TV wasn't really the same after that.

The shows that depicted the Black characters I loved and enjoyed, weren't available in the country. Sometimes if they were, they would be 3-4 episodes behind from the American releases. At this point, I relied on the internet, or my brother’s video collections to air the shows I missed so much. Black representation was never really viewed as much as I was exposed to in the States. It became non-existent to me. The only time I would see a Black person in media or TV, is when a creative artist becomes popular, and outlets find out they're from Montreal. It was difficult to find representation growing up in this city, I always felt like we were side characters in our own adventures. People don't realize it but it does have an affect on people. It's nice to know that organizations are creating platforms for BIPOC representation, because we exist and we are not alone.

➡ RSVP ⬅

#2020 #kai samuels #juice #nafleri #hasina kamanzi #blazenblack #joseph moore #simone heath

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homoground · 7 years ago

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"I recently attended MASSIMADI, a film festival celebrating queerness in Afro and Afro-Caribbean communities and a friend shared a story of how it is still a survival match in some Caribbean islands. I feel that spreading queer Caribbean and queer Caribbean- Canadian stories using my voice is as important as the literature I currently analyze for my Master Degree in Literature. Homoground's celebration of queer music artists not only gives me a platform to further my music advanture, but also to share these stories." -Michael Perry @michaelperryjr Listen to more of Michael's experience and music on episode #215 http://ift.tt/2wWeGVn http://ift.tt/2wUO9fY http://ift.tt/2hzxlAT

#homoground #lgbtq #lgbt #queer #queermusic

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valiantdreamlandblizzard-blog1 · 4 years ago

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Assignment 3

Animal phobia

In my literature review I mentioned a study that made a distinction between pure and mixed (that is combined with some other specific phobias) animal phobia. So I thought it may be instructive to also look at these two types separately. This required a number of tricks, which are supposedly called data management. These steps were:

Take the values from all specific phobia variables (there are 11 of them including animal phobia) and store them in new columns having replaced all ‘No-s’ and ‘Unknowns’ with 0 (so that they only have 1 if there was this phobia experience and 0 if there was not or it is unknown);

Create a new column and fill it with the result of summing up all rows within those recoded special phobia columns;

Take a subset of my dataset, which only includes respondents with animal phobia experience (that is all values in the correspondent column == 1);

Recode the new column with summed results so that it only keeps 1 values and those greater than 1 (indicating there are other phobias apart from animals) are 0;

Use this recoded column to distinguish pure and mixed cases of animal phobia.

Here is the code snippet:

# Create new columns to store recoded values for different kinds of specific phobia for phobia in ALL_SPECIFIC_PHOBIAS: data[phobia[CODE] + '_NEW'] = data[phobia[CODE]].replace([2, 9], 0) # Sum up all values for phobias in new columns and store the result in a new column 'APPUREMIXED' data[APPUREMIXED] = data.loc[:, sp_new_list].sum(axis=1) condition_for_replace = data[APPUREMIXED] > 1 data.loc[condition_for_replace, APPUREMIXED] = 0 # replace values > 1 with 0 appuremixed_freq = data[data[ANIMALS_MAP[CODE]] == 1][APPUREMIXED].value_counts(sort=False, dropna=False) appuremixed_percent = data[data[ANIMALS_MAP[CODE]] == 1][APPUREMIXED].value_counts(sort=False, dropna=False, normalize=True) print('\nFrequencies, percentages for pure and mixed animal phobia') print(pd.concat(dict(Frequencies=appuremixed_freq.rename({1: 'Pure', 0: 'Mixed'}), Percentages=appuremixed_percent.rename({1: 'Pure', 0: 'Mixed'})), axis=1))

The resulting frequency distributions:

Frequencies, percentages for pure and mixed animal phobia Frequencies Percentages Mixed 6836 0.751787 Pure 2257 0.248213

I wonder if it could be done in an simpler way.

Origin or descent

With origins it was even trickier. I wanted to see the percentages of the respondents with animal phobia for each kind of origin separately. But I failed to find a way to do it based on this dataset. I am sure there are better ways to handle this (maybe via grouping? but again, I still do not quite understand the mechanics). So I just created a new dataframe to store all necessary data to calculate these percentages. Here are the steps:

Get frequencies for origins;

Get frequencies for origins for the respondents with animal phobia;

Combine these two results into a new dataframe with origin names as indices and two frequencies variables as columns;

Calculate percentages and store them in a new column.

I also replaced Unknown and Other origins with NaN values and dropped them when creating the new dataframe.

Code snippet:

# Convert Unknown and Other to NaN data[ORIGIN_MAP[CODE]] = data[ORIGIN_MAP[CODE]].replace([98, 99], np.nan) # Get frequencies by origin origins = data[ORIGIN_MAP[CODE]].value_counts(sort=False, dropna=True) # Get origin frequencies based on the condition that respondents have animal phobia condition = data[ANIMALS_MAP[CODE]] == 1 origins_with_ap = data[condition][ORIGIN_MAP[CODE]].value_counts(sort=False, dropna=True) # Create a new dataframe out of these two frequency series origins_df = origins.rename(ORIGIN_MAP[VALUES]).to_frame(name='ORIGCOUNTS') origins_df['ORIGAPCOUNTS'] = origins_with_ap.rename(ORIGIN_MAP[VALUES]) # Create a new column in this new df to store percentages origins_df['APPERCENT'] = origins_df['ORIGAPCOUNTS'] / origins_df['ORIGCOUNTS']

And here is the top and the bottom of the sorted output (the printed the column with percentages):

Turkish 0.315789 African American (Black, Negro, or Afro-American) 0.301406 Other Caribbean or West Indian (Spanish Speaking) 0.291667 Filipino 0.269058 African (e.g., Egyptian, Nigerian, Algerian) 0.264706 Guamanian 0.263158 Vietnamese 0.257426 Other Spanish 0.253623 Other Caribbean or West Indian (Non-Spanish Speaking) 0.252475 Canadian 0.250000 ... Israeli 0.148936 Russian 0.138756 Indonesian 0.137931 Chinese 0.133987 Other Eastern European (Romanian, Bulgarian, Albanian) 0.114035 Iranian 0.106383 Iraqi 0.100000 Samoan 0.100000 Jordanian 0.090909 Australian, New Zealander 0.078947

As was shown in my previous assignment, the overall percentage of animal phobia was 21%. On the origin level though these percentages demonstrate considerable variety. There are much lower values for some (like Australian, New Zealander, about 8%) and higher values for others (e.g. African American, 30%).

However, these results may have different weight, so to speak. For example, we see that the Turkish origin is on the very top with about 32% of animal phobia rate. But there are only 19 respondents with this origin for the whole dataset, and 6 of them had this animal fear experience. One might doubt that on such a tiny sample the result might be trustworthy. On the other hand, there are African Americans, who are really numerous (7684).

That is why I decided to work only with a subset of those origins, which have 400 or more occurrences in the dataset. I chose 400 as a threshold, because it is kind of a magic number in the research area (based on sample size calculations and confidence intervals).

Here is the code:

subset_orig_gte_400 = origins_df[origins_df['ORIGCOUNTS'] >= 400].copy() print('Origins subset (gte 400 respondents)') print(subset_orig_gte_400.sort_values(by=['APPERCENT'], ascending=False))

As a result I got a smaller subset (20 rows instead of 60) with the following animal phobia shares:

African American (Black, Negro, or Afro-American) 0.301406 American Indian (Native American) 0.241026 South American (e.g., Brazilian, Chilean, Columbian) 0.232323 Central American (e.g., Nicaraguan, Guatemalan) 0.228361 Puerto Rican 0.220662 Dutch 0.203980 French 0.201336 Spanish (Spain) , Portugese 0.198819 Italian 0.198071 Irish 0.187867 Scottish 0.187335 English 0.185410 Cuban 0.184444 Mexican-American 0.184300 Norwegian 0.184211 Mexican 0.183088 German 0.179607 Swedish 0.178654 Polish 0.176768 Russian 0.138756

Here I recall a valuable input by a peer who commented on my Assignment 2 and, among all, mentioned that some Native Americans may have higher animal fear rate.

It is also worth noting that none of the origins showed any extraordinary animal phobia rate, like 50% or higher.

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krishg2000 · 4 years ago

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Assignment 3 DATA MANAGMENT AND VISUALIZATION

The instructions regarding the blog post I also found a bit vague. In fact, the presentation requirements were just like last time:

The script;

The output (“that displays at least 3 of your data managed variables as frequency distributions”);

Some comments (“describing these frequency distributions in terms of the values the variables take, how often they take them, the presence of missing data, etc.”).

And below is what I have done about my variables so far.

Like previously, I stick to my three basic variables from NESARC dataset, which are:

The experience of animal phobia;

The origin (or descent);

Perceived health.

But this time I decided to use some other variables for comparison.

Animal phobia

In my literature reviewI mentioned a study that made a distinction between pure and mixed (that is combined with some other specific phobias) animal phobia. So I thought it may be instructive to also look at these two types separately. This required a number of tricks, which are supposedly called data management. These steps were:

Create a new column and fill it with the result of summing up all rows within those recoded special phobia columns;

Take a subset of my dataset, which only includes respondents with animal phobia experience (that is all values in the correspondent column == 1);

Recode the new column with summed results so that it only keeps 1 values and those greater than 1 (indicating there are other phobias apart from animals) are 0;

Use this recoded column to distinguish pure and mixed cases of animal phobia.

Here is the code snippet:

The resulting frequency distributions:

Frequencies, percentages for pure and mixed animal phobia Frequencies Percentages Mixed 6836 0.751787 Pure 2257 0.248213

I wonder if it could be done in an simpler way.

Origin or descent

Get frequencies for origins;

Get frequencies for origins for the respondents with animal phobia;

Combine these two results into a new dataframe with origin names as indices and two frequencies variables as columns;

Calculate percentages and store them in a new column.

I also replaced unknow and other origins with NaN values and dropped them when creating the new dataframe.

Code snippet:

And here is the top and the bottom of the sorted output (the printed the column with percentages):

As in the previous assignment, the overall percentage of animal phobia was 21%. On the origin level though these percentages demonstrate considerable variety. There are much lower values for some (like Australian, New Zealander, about 8%) and higher values for others (e.g. African American, 30%).

Here is the code:

subset_orig_gte_400 = origins_df[origins_df['ORIGCOUNTS'] >= 400].copy() print('Origins subset (gte 400 respondents)') print(subset_orig_gte_400.sort_values(by=['APPERCENT'], ascending=False))

As a result I got a smaller subset (20 rows instead of 60) with the following animal phobia shares:

Here I recall a valuable input by a peer who commented on my Assignment 2 and, among all, mentioned that some Native Americans may have higher animal fear rate.

It is also worth noting that none of the origins showed any extraordinary animal phobia rate, like 50% or higher.

Perceived health

For the perceived health variable I also recoded all Unknowns into NaN, just in case, and then dropped them.

What is more impressive, I had a look at perceived health distribution for those with pure animal phobia. As in my previous assignment, I compared the distribution across the whole dataset with the distribution for those with animal phobia. There was some difference (in particular, the percentage of those whose perceived health is poor, was slightly higher, 7% vs. 5%).

So, this time I calculated perceived health distribution for pure animal phobia and compared it with previous calculations. Code:

data[HEALTH_MAP[CODE]] = data[HEALTH_MAP[CODE]].replace(9, np.nan) # Get percentages for perceived health distribution (for all) health_percent = data[HEALTH_MAP[CODE]].value_counts(sort=False, dropna=True, normalize=True) # health perception vs. animal phobia health_ap_percent = data[data[ANIMALS_MAP[CODE]] == 1][HEALTH_MAP[CODE]].value_counts(sort=False, dropna=True, normalize=True) # health perception vs. pure animal phobia health_pure_ap_percent = data[(has_ap & has_pure_ap)][HEALTH_MAP[CODE]].value_counts(sort=False, dropna=True, normalize=True) print('\nCompared distribution percentages for Perceived Health') print(pd.concat(dict(Dataset=health_percent.rename(HEALTH_MAP[VALUES]), AnimalPhobia=health_ap_percent.rename(HEALTH_MAP[VALUES]), PureAnimalPhobia=health_pure_ap_percent.rename(HEALTH_MAP[VALUES])), axis=1))

Result:

Compared distribution percentages for Perceived Health AnimalPhobia Dataset PureAnimalPhobia Excellent 0.228515 0.287576 0.299777 Fair 0.159652 0.121862 0.095323 Good 0.271926 0.248652 0.249889 Poor 0.072829 0.051813 0.044989 Very good 0.267078 0.290097 0.310022

As we can see, the share of those who perceive their health as poor is the smallest in the case of pure animal phobia. These respondents also most often perceive their health as excellent or very good. Actually this reminds me of the study Pure animal phobia is more specific than other specific phobias by Vladeta Ajdacic-Gross et al., which states that “Pure animal phobia showed no associations with any included risk factors and comorbid disorders, in contrast to numerous associations found in the mixed subtype and in other specific phobias”.

Wrap up

The attempt to distinguish pure and mixed animal phobia showed the proportion of 25% (pure) vs. 75% (mixed). While processing other special phobias data I faced a dilemma on how to treat missing values (or Unknown). I saw two ways:

Code all Unknowns as NaN to make sure I only count the results for those cases that are definitely true. This approach would imply that pure animal phobia is the one, about which we are absolutely sure that it is not combined with any other specific phobias.

Code all No-s and Unknowns as 0 and treat them equally. This would imply that pure animal phobia is the one, about which we have no evidence that it is combined with any other specific phobias.

I chose the latter. First, the approach with NaN would lead to a messier picture with lots of uncertainties to be taken into consideration. Second, and even more important, I cannot be sure that this dataset lists all possible specific phobias. By the way, I failed to find something like pyrophobia there. So, even if I try and clean out all Unknowns on the dataset level, there will still be huge unknowns outside its scope. That is why decided not to make any difference between No and Unknown when recoding these variables.

With the origins variable I ended up with a subset of those with 400 or more occurrences as most representative. The top-3 origins by animal phobia percentage were African American (30%), American Indian (24%), South American (23%). The lowest percentage is in the cases of Russian (14%), Polish (18%) and Swedish (18%). Looks like there may be some geographic pattern indeed. Although we can see that Cuban, Mexican and Mexican-American origins (that is southern) are somewhere in the middle. I might also want to later have a look at the distribution of pure animal phobia across the origins.

As to the perceived health variable, I compared the results for the whole dataset with the results for those with animal phobia and with pure animal phobia. I was surprised to see that those with pure animal phobia tend to estimate their health better than the others: Poor: 7% for the respondents with animal phobia, including mixed cases; 5% for the whole dataset; 4% for those with pure animal phobia. And excellent: 23% (animal phobia including mixed); 29% (whole dataset); 30% (pure animal phobia).

#database #visualization

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jessicakehoe · 5 years ago

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You Can Shop Canadian-Made, Eco-Friendly Brands At This New Toronto Pop-Up

With the long weekend just days away, we’re busy thinking of plans to fill the three days out of the office. One can’t-miss event this weekend? The Pretty Elevated By Irisa pop-up at Stackt Market.

The pop-up brings together some of Canada’s most exciting female-run businesses and is described as “a community driven collective and social impact initiative dedicated to empowering women and creating opportunities that amplify the female voice through community and connectivity.”

The pop-up, led by Canadian cannabis brand Irisa, has been running for just over a week and will continue until September 22nd. The pop-up is offering a rotating selection of brands for consumers to discover and shop, alongside an extensive programme of workshops and talks by various local creatives and life coaches. There’s also a library packed with feminist literature and a co-working space if you’re feeling inspired and want to surround yourself with like-minded women.

This weekend you’ll be able to shop Bag and Bougie, a range of durable, eco-friendly bags; Peoples Products, fair-trade clothing made by women for women; and Cherry Gardens, a loungewear and underwear collection. Following their stint in the pop-up, Threads, an ethically sourced, subscription tights brand, Canadian vegan beauty brand Sarisha Beauty and Eugenia Chan jewellery will move in. The space has already played host to digital-first clothing brand BirdieFit, afro-furturist jewellery and clothing label Wild Moon and Toronto-born swimwear line Smitten Swim.

One of the driving forces behind the pop-up was the skyrocketing price of retail space within Toronto which is prohibitive for many brands. Adine Fabiani-Carter, the chief marketing office at High Park (the event’s parent company), said in a statement, “Pretty Elevated is about creating a space and community that empowers women to pursue personal and professional growth. We’re here to support local entrepreneurs by giving them a platform to amplify their businesses and expand their audience. We invite and encourage people in the city of Toronto to join us in celebrating these great local businesses.” Shopping for a cause? Now that’s definitely something we (and our bank accounts) can get on board with.

The post You Can Shop Canadian-Made, Eco-Friendly Brands At This New Toronto Pop-Up appeared first on FASHION Magazine.

You Can Shop Canadian-Made, Eco-Friendly Brands At This New Toronto Pop-Up published first on https://borboletabags.tumblr.com/

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londontheatre · 7 years ago

Link

HeadFirst Productions presents a ten-day multi-arts festival A Festival of Sex, Love and Death at the Pleasance Theatre Islington.

From 24 October – 4 November 2017, HeadFirst Productions presents a new multi-arts festival at The Pleasance Theatre, titled A Festival of Sex, Love and Death. The festival brings five events from eight companies, including three double bills, to Islington for ten days this autumn. The programme champions the work of young emerging creatives working all over the UK in a range of genres including opera, dance, cabaret, physical theatre, new writing and spoken word. Each piece will demonstrate a unique response to the festival’s themes, sex, love and death – arguably the thematic foundations of the festival’s flagship production: Mozart’s 18th century masterpiece Don Giovanni.

HeadFirst Productions’ Artistic Director, Sophie Gilpin, says, “Sex, love and death are key forces that drive how we perceive and experience our lives. The festival serves to express varying perspectives on the fundamental aspects of life that make us who we are, and showcases the work of some incredibly exciting and innovative theatre companies. It’s wonderful to be able to bring such a diverse range of events to Islington’s already thriving arts scene, and in particular to introduce opera to the Pleasance for the very first time.”

Artists will come together under a single umbrella to respond in a unique and thrilling way to the festival’s themes, with a particular focus on female-led, POC-led and LGBT+ narratives. Companies and theatre makers involved are Liver and Lung Productions, Ella Mesma Company, Ruby in the Dust Theatre, Created a Monster Theatre, Hatch It Theatre, Oskar McCarthy/Rob Keeley and David Levesley/Juliet Clark. Each production team will make use of a single, highly adaptable set designed by Anna Bonomelli; the visual through line will contribute to an artistic and thematic cohesiveness whilst the flexible design will enable – and encourage – each company to utilise and respond in its own unique way.

The festival will enable the cross-fertilisation of audiences through its varied yet complementary programme and affordable ticket prices – for as little at £10 each – all delivered at a carefully selected venue with a proven track record of presenting high-quality contemporary work.

PROGRAMME

Sex, Love & Death: Don Giovanni This operatic masterpiece grapples with the complexity of human nature; contradictions, hidden desires, and darkest fears. By exploring the conflict between immorality and amorality, and – crucially – by telling the story from the women’s perspective, this production will steer away from the opera’s traditionally male gaze, and leave the audience questioning their preconceptions. Returning to London after 4* and 5* reviews for La Bohème in 2014, HeadFirst Productions promises provocation, dark humour and sensuality. Sung in Italian with English surtitles and performed with a small orchestral ensemble. (HeadFirst Productions – OPERA) When: 26th, 29th (6pm), 30th October & 1st, 3rd November at 7.30pm

Sex: Submission & Ladylike Submission tells the story of Sameer, a young British Pakistani, who struggles to reconcile his sexual desires with a religion he values, admires and cherishes. Told through the contrasting lenses of spoken word, multi-role play and naturalistic dialogue, Submission is a powerful, poignant and purposeful piece of new writing. Shedding light on highly relevant issues, Submission screams what needs to be said in a time when integration and tolerance is needed more than ever. (Liver & Lung Productions – DRAMA/SPOKEN WORD)

Ladylike is an absurd, comical, and emotionally charged ritual of dance theatre. In a landscape of Afro-Latin and Hip Hop, four females cluck, fight & undress our preconceptions of what it is to be ‘ladylike’. Each explores our personal experiences of sexuality, pleasure and consent, and they come together to challenge & celebrate one another as the heroine in the ultimate hen party. (Ella Mesma Company – DANCE) When: 2nd November at 7.30pm

Love: The Extraordinary Cabaret of Dorian Gray Set in the last few delirious hours of his life, Dorian Gray watches as his decadent life flashes before him. What he glimpses is a far cry from the opera houses, the salons and the gentlemen’s clubs. Join him as he delves into the darker, murkier underbelly of London: the cabaret clubs and opium dens, where sex and death share the same bed. And where love dare not speak its name. Descend into his world – the real picture of Dorian Gray – beneath his youthful mask. Adapted from Oscar Wilde’s only novel, tracing the life of one of literature’s most notorious characters, the tale reveals each character’s quest for love: the ultimate sensation! With original music performed by a cast of actor/musicians and cabaret artists. (Ruby in the Dust Theatre – CABARET/NEW WRITING) When: 27th October & 4th November at 7.30pm

Death: Buried Alive & Mortgage Buried Alive is a semi-staged recital of 14 poems by Gottfried Keller (1819-1890) with music by Othmar Schoeck (1886-1957). In 1927 Swiss composer Othmar Schoeck adapted the poetry of his fellow countryman Gottfried Keller to produce the song cycle Lebendig Begraben [Buried Alive] which tells the story of a man who awakes to find he has mistakenly been buried. Firstly he panics and hopes that his girlfriend or a friendly grave robber will come to his rescue; then he begins to reminisce in his coffin about his childhood, youth and first love; finally he casts his soul into eternity in an acceptance of his fate.

(Oskar McCarthy & Rob Keeley – SEMI-STAGED RECITAL) A furiously visceral show, Mortgage tells the unbearably tragi-comic story of the painfully abused and miserable short and boring life of Beatrice Gunta Mortgage – Stage Manager. In this collaboration between Created a Monster and the David Glass Ensemble, Mortgage will attempt to overcome madness, misery and possession in a piece of dynamic new performance that explores a culture of managing art, hopelessness and the will to overcome all. Inspired by Peter Brook’s notion of living and deadly theatre, Artaud’s ‘Theatre of Cruelty’, magic, and sacrifice, Mortgage will see Lecoq-trained performer Briony O’Callaghan collaborate with David Glass to create an inspiring, visually arresting and moving new work about the violent and necessary relationship between creation and destruction. (Created A Monster Theatre – PHYSICAL THEATRE) When: 28th October at 7.30pm

Sex & Death: Whalebone & Silent Meat Whalebone is about bodies: who takes up space, how much, and why. Three puppeteers stand awkwardly in corsets. A woman decides to take control of her body – deleting it, piece by piece. Reimagining Lolita’s lesser-known sister, Nabokov’s Laura, Whalebone collides puppetry and physical theatre in a world where bodies are painted, tucked, tightened and taught, where shadows are embarrassing and silhouettes become stencils. Irreverent feminist theatre, narrated by a talking vagina. (Hatch It Theatre – PHYSICAL THEATRE/PUPPETRY)

A gay couple meet in what feels like a destined encounter, only for their ideas of faith to be put to the test. An American PR executive searches for the answers to the death of a girl she’s never met. An elderly woman tries to reconnect with her childhood friend before euthanising herself with Mexican pet barbiturates. And, in Tel Aviv, a peppy Canadian vlogger moves in with her Israeli holiday fling, only for him to be called up to fight in Gaza. What does it feel like to discover your partner died via a google search? Why do people become obsessed with unsolved deaths on Reddit? What happens when moving in with your boyfriend means being accused of war crimes on Facebook? Set across three continents, Silent Meat is an exploration of what it feels like to love, lose and feel alone in the 21st century. (David Levesley & Juliet Clark – NEW WRITING) When: 31st October at 7.30pm

Pleasance Theatre Carpenters Mews North Road N7 9EF Thursday 26th October – Saturday 4th November 7.30pm (Sunday 29th October, 6pm) Box Office: pleasance.co.uk More information: headfirstproductions.org/

http://ift.tt/2jW2LWx LondonTheatre1.com

#IFTTT #LondonTheatre1.com

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yourchaodufan-blog · 7 years ago

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1. I would like to choose data set NESARC because i am interested in how alcohol would affect people but I am not sure what kind of variables i should use.

2. I want to start with alcohol dependence. It strikes me that friends and acquaintances that I have known through the years that became addicted on alcohol. Some seemed to be dependent soon after their first few experiences with drinking and others after many years of generally irregular drinking behavior.

I decide that I am most interested in exploring the association between race and alcohol dependence. I add to my codebook variables reflecting alcohol levels and races.

codebook:

------------------------------------------------------------------------------------------------------------------------------------ Tape Location Source Code Frequency Item value and description ------------------------------------------------------------------------------------------------------------------------------------ SECTION 1: BACKGROUND INFORMATION ------------------------------------------------------------------------------------------------------------------------------------ 1-5 IDNUM UNIQUE ID NUMBER WITH NO ALPHABETICS ------------------------------------ 43093 1-43093. Unique Identification number ------------------------------------------------------------------------------------------------------------------------------------ 6-10 PSU PSU --- 43093 1001-56017. Psu ------------------------------------------------------------------------------------------------------------------------------------ 11-14 STRATUM STRATUM ------- 43093 101-5605. Stratum ------------------------------------------------------------------------------------------------------------------------------------ 15-32 WEIGHT FINAL WEIGHT [Format: XXXXX.XXXXXXXXXXXX] ---------------------------- 43093 398.03738221-57902.204788. Weighting factor ------------------------------------------------------------------------------------------------------------------------------------ 33-34 CDAY DATE OF INTERVIEW: DAY ---------------------- 43093 1-31. Day ------------------------------------------------------------------------------------------------------------------------------------ 35-36 CMON DATE OF INTERVIEW: MONTH ------------------------ 43093 1-12. Month ------------------------------------------------------------------------------------------------------------------------------------ 37-40 CYEAR DATE OF INTERVIEW: YEAR ----------------------- 36992 2001. 2001 6101 2002. 2002 ------------------------------------------------------------------------------------------------------------------------------------ 41-41 REGION CENSUS REGION ------------- 8209 1. Northeast 8991 2. Midwest 16156 3. South 9737 4. West ----------------------------------------------------------

- 81-81 S1Q1C HISPANIC OR LATINO ORIGIN ------------------------- 8308 1. Yes 34785 2. No ------------------------------------------------------------------------------------------------------------------------------------ 82-82 S1F1C IMPUTATION FLAG FOR HISPANIC ORIGIN ----------------------------------- 42949 0. No 144 1. Yes ------------------------------------------------------------------------------------------------------------------------------------ 83-83 S1Q1D1 "AMERICAN INDIAN OR ALASKA NATIVE" CHECKED IN MULTIRACE CODE ------------------------------------------------------------ 1304 1. Yes 41789 2. No ------------------------------------------------------------------------------------------------------------------------------------ 84-84 S1Q1D2 "ASIAN" CHECKED IN MULTIRACE CODE --------------------------------- 1334 1. Yes 41759 2. No ------------------------------------------------------------------------------------------------------------------------------------ 85-85 S1Q1D3 "BLACK OR AFRICAN AMERICAN" CHECKED IN MULTIRACE CODE ----------------------------------------------------- 8600 1. Yes 34493 2. No ------------------------------------------------------------------------------------------------------------------------------------ 86-86 S1Q1D4 "NATIVE HAWAIIAN OR OTHER PACIFIC ISLANDER" CHECKED IN MULTIRACE CODE --------------------------------------------------------------------- 363 1. Yes 42730 2. No ------------------------------------------------------------------------------------------------------------------------------------ 6 ------------------------------------------------------------------------------------------------------------------------------------ Tape Location Source Code Frequency Item value and description ------------------------------------------------------------------------------------------------------------------------------------ 87-87 S1Q1D5 "WHITE" CHECKED IN MULTIRACE CODE --------------------------------- 32789 1. Yes 10304 2. No ------------------------------------------------------------------------------------------------------------------------------------ 88-88 S1F1D IMPUTATION FLAG FOR RACE ------------------------ 42476 0. No 617 1. Yes ------------------------------------------------------------------------------------------------------------------------------------ 89-90 S1Q1E ORIGIN OR DESCENT ----------------- 7684 1. African American (Black, Negro, or Afro-American) 272 2. African (e.g., Egyptian, Nigerian, Algerian) 975 3. American Indian (Native American) 38 4. Australian, New Zealander 128 5. Austrian 56 6. Belgian 256 7. Canadian 543 8. Central American (e.g., Nicaraguan, Guatemalan) 222 9. Chicano 306 10. Chinese 450 11. Cuban 299 12. Czechoslovakian 178 13. Danish 603 14. Dutch 4455 15. English 223 16. Filipino 110 17. Finnish 1048 18. French 5345 19. German 136 20. Greek 19 21. Guamanian 186 22. Hungarian 251 23. Indian, Afghanistani, Pakistani 29 24. Indonesian 47 25. Iranian 20 26. Iraqi 3066 27. Irish 47 28. Israeli 1555 29. Italian 175 30. Japanese 11 31. Jordanian

454-454 S2BQ1A1 EVER FIND USUAL NUMBER OF DRINKS HAD LESS EFFECT THAN BEFORE ------------------------------------------------------------ 7175 1. Yes 27147 2. No 505 9. Unknown 8266 BL. NA, lifetime abstainer ------------------------------------------------------------------------------------------------------------------------------------ 455-455 S2BQ1B1 HAPPEN IN THE LAST 12 MONTHS ---------------------------- 1326 1. Yes 25309 2. No 311 9. Unknown 16147 BL. NA, lifetime abstainer or former drinker ------------------------------------------------------------------------------------------------------------------------------------ 456-456 S2BQ1C1 HAPPEN PRIOR TO LAST 12 MONTHS ------------------------------ 6596 1. Yes 27714 2. No 517 9. Unknown 8266 BL. NA, lifetime abstainer ------------------------------------------------------------------------------------------------------------------------------------ 47 ------------------------------------------------------------------------------------------------------------------------------------ Tape Location Source Code Frequency Item value and description ------------------------------------------------------------------------------------------------------------------------------------ 457-457 S2BQ1A2 EVER HAD TO DRINK MORE TO GET THE EFFECT WANTED ----------------------------------------------- 4499 1. Yes 29949 2. No 379 9. Unknown 8266 BL. NA, lifetime abstainer ------------------------------------------------------------------------------------------------------------------------------------ 458-458 S2BQ1B2 HAPPEN IN THE LAST 12 MONTHS ---------------------------- 807 1. Yes 25919 2. No 220 9. Unknown 16147 BL. NA, lifetime abstainer or former drinker ------------------------------------------------------------------------------------------------------------------------------------ 459-459 S2BQ1C2 HAPPEN PRIOR TO LAST 12 MONTHS ------------------------------ 4137 1. Yes 30308 2. No 382 9. Unknown 8266 BL. NA, lifetime abstainer ------------------------------------------------------------------------------------------------------------------------------------ 460-460 S2BQ1A3 EVER DRINK EQUIVALENT OF A FIFTH OF LIQUOR IN ONE DAY ----------------------------------------------------- 3385 1. Yes 31176 2. No 266 9. Unknown 8266 BL. NA, lifetime abstainer ------------------------------------------------------------------------------------------------------------------------------------ 461-461 S2BQ1B3 HAPPEN IN THE LAST 12 MONTHS ---------------------------- 665 1. Yes 26134 2. No 147 9. Unknown 16147 BL. NA, lifetime abstainer or former drinker ------------------------------------------------------------------------------------------------------------------------------------ 462-462 S2BQ1C3 HAPPEN PRIOR TO LAST 12 MONTHS ------------------------------ 3209 1. Yes 31349 2. No 269 9. Unknown 8266 BL. NA, lifetime abstainer

Second topic: I want to say if education has a big impact on alcohol dependence so I want to add those variables in codebook.

131-132 S1Q6A HIGHEST GRADE OR YEAR OF SCHOOL COMPLETED ----------------------------------------- 218 1. No formal schooling 137 2. Completed grade K, 1 or 2 421 3. Completed grade 3 or 4 931 4. Completed grade 5 or 6 414 5. Completed grade 7 1210 6. Completed grade 8 4518 7. Some high school (grades 9-11) 10935 8. Completed high school 1612 9. Graduate equivalency degree (GED) 8891 10. Some college (no degree) 3772 11. Completed associate or other technical 2-year degree 5251 12. Completed college (bachelor's degree) 1526 13. Some graduate or professional studies (completed bachelor's degree but not graduate degree) 3257 14. Completed graduate or professional degree (master's degree or higher) ------------------------------------------------------------------------------------------------------------------------------------ 133-133 S1F6A IMPUTATION FLAG FOR HIGHEST GRADE OR YEAR OF SCHOOL COMPLETED ------------------------------------------------------------- 42624 0. No 469 1. Yes ------------------------------------------------------------------------------------------------------------------------------------ 134-135 S1Q6B AGE WHEN COMPLETED HIGHEST GRADE OR YEAR OF SCHOOL -------------------------------------------------- 41523 5-89. Age 1352 99. Unknown 218 BL. NA, no formal schooling ---------------------------------------------------------------------------

literature review:

I have reviewed a literature which has a very similar topic that “Alcohol Use Disorders Among US College Students and Their Non–College-Attending Peers”.

“The conclusion is college students suffer from some clinically significant consequences of their heavy/binge drinking, but they do not appear to be at greater risk than their non–college-attending peers for the more pervasive syndrome of problems that is characteristic of alcohol dependence”(Slutske,2005).

Regard to the literature, my hypothesis is that the education may not have significant association with alcohol dependence.

Some people with higher education may not have alcohol dependence but overall, the education may not have significant association with alcohol dependence.

References:

Slutske, PhD Wendy S. "Alcohol Use Disorders Among US College Students and Their Non–College-Attending Peers." Archives of General Psychiatry. American Medical Association, 01 Mar. 2005. Web. 07 July 2017.

http://jamanetwork.com/journals/jamapsychiatry/fullarticle/208365

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laparieee-blog · 8 years ago

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Post 7: MLA International Bibliography

“Afro-Caribbean Spirituality in Carmen Montanez’s Pelo bueno, pelo malo”

Thomas Edison reviews Carmen Montanez's novel by examining the theme of spirituality within the text. As Pelo bueno, pelo malo focuses on the society's perception of which hair textures are desirable and which are not, Edison recognizes the symbolism of spirituality, which the main character, Amarilis, uses as a method of healing that interrupts her need to straighten her hair in order to be considered more valuable.

“Scholarly Publishing: Caribbean Publishing, 1711-1900: A Preliminary Subject Analysis”

Lishi Kwasitsu evaluates the books and pamphlets that were published in the West Indies and investigates the subject matter, social, and politico-economic circumstances found within their production. The years range from two centuries and each research piece results in a variety of findings, including the customs and folklores of the Indian population, debates and arguments amongst the "colonizing powers" of European regions, and the exploration of Caribbean islands (I.e., Cuba, Puerto Rico, Jamaica). EXTREMELY INTERESTING ARTICLE.

If you are like me, this article will probably make you hate Christopher Columbus more than you probably do (or should).

“Haitians, Cocolos, and African Americans: Early Authors of Contemporary Afro-Dominican Literature”

Dawn F. Stinchcomb discusses the works of Afro-Dominican authors whose genres vary within contemporary literature. These authors are recognized as being prominent in the late 19th century and early 20th century, and include writers such as Juan Sanchez Lamouth, Jacques Viau Renaud, and Norborto James Rawlings. Each authors' works implement cultural conditions of an Afro-Latin perspective. The authors each concur that there is a great misrepresentation of the Dominican culture within media, thus they challenge this notion and bring awareness to terminology used to describe persons of Afro-Caribbean descent, like Cocolos to portray Haitians. This article breaks down the history of Afro-heritage and expands on this topic through literature.

“‘Revolutionary Viragoes’: Othered Mothering in Afro-Caribbean Diaspora Literature”

Another article reviewing literature within the Afro-Caribbean, Nancy Kang speaks about works of Black writers and the ways in which they express their experiences as Black Caribbeans through stories. The article introduces the concept of literary migration, which is viewed as the status quo of Black writers who write from perspectives of Canadian and Latin identities.

“The Secret History of the Early American Novel: Leonora Sansay and Revolution in Saint Domingue”

Elizabeth Maddock Dillon reviews the novel Secret History: or, The Horrors of St. Domingo by Leonora Sansay. The novel is written in the form of letters and explores the personal experience of Sansay's life in Santo Domingo in the early 1800s. As Dillon points out that this novel has gone overlooked by scholars who study early American literature, Sansay's work should be acknowledged for its attention to "colonial and creole social reproduction" (79).

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krishg2000 · 4 years ago

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Assignment 3 data management and visualization.

In fact, the presentation requirements were just like last time:

The script;

The output (“that displays at least 3 of your data managed variables as frequency distributions”);

Some comments (“describing these frequency distributions in terms of the values the variables take, how often they take them, the presence of missing data, etc.”).

And below is what I have done about my variables so far.

Like previously, I have done to my three basic variables from NESARC dataset, which are:

The experience of animal phobia;

The origin ;

Perceived health.

But this time I decided to use some other variables for comparison.

Animal phobia

In my literature view in assignment 1. I mentioned a study that made a distinction between pure and mixed animal phobia. So I thought it may be instructive to also look at these two types separately. This required a number of tricks, which are supposedly called data management. These steps were:

Create a new column and fill it with the result of summing up all rows within those recoded special phobia columns;

Take a subset of my dataset, which only includes respondents with animal phobia experience (that is all values in the correspondent column == 1);

Recode the new column with summed results so that it only keeps 1 values and those greater than 1 (indicating there are other phobias apart from animals) are 0;

Use this recoded column to distinguish pure and mixed cases of animal phobia.

Here is the code :

The resulting frequency distributions:

Frequencies, percentages for pure and mixed animal phobia Frequencies Percentages Mixed 6836 0.751787 Pure 2257 0.248213

I wonder if it could be done in an simpler way.

Origin or descent

With origins it was even trickier. I wanted to see the percentages of the respondents with animal phobia for each kind of origin separately. But I failed to find a way to do it based on this dataset. I am sure there are better ways to handle this. So, I just created a new dataframe to store all necessary data to calculate these percentages. Here are the steps:

Get frequencies for origins;

Get frequencies for origins for the respondents with animal phobia;

Combine these two results into a new dataframe with origin names as indices and two frequencies variables as columns;

Calculate percentages and store them in a new column.

I also replace the unknown and other origins with Nan values and dropped them when creating the new dataframe.

Code snippet:

And here is the top and the bottom of the sorted output:-

As was shown in my previous assignment , the overall percentage of animal phobia was 21%. On the origin level though these percentages demonstrate considerable variety. There are much lower values for some (like Australian, New Zealander, about 8%) and higher values for others (e.g. African American, 30%).

Here is the code:

subset_orig_gte_400 = origins_df[origins_df['ORIGCOUNTS'] >= 400].copy() print('Origins subset (gte 400 respondents)') print(subset_orig_gte_400.sort_values(by=['APPERCENT'], ascending=False))

As a result I got a smaller subset (20 rows instead of 60) with the following animal phobia shares:

Here I recall a valuable input by a peer who commented on my Assignment 2 and, among all, mentioned that some Native Americans may have higher animal fear rate.

It is also worth noting that none of the origins showed any extraordinary animal phobia rate, like 50% or higher.

Perceived health

For the perceived health variable I also recoded all Unknowns into NaN, just in case, and then dropped them.

What is more impressive, I had a look at perceived health distribution for those with pure animal phobia. In my previous assignment, I compared the distribution across the whole dataset with the distribution for those with animal phobia. There was some difference (in particular, the percentage of those whose perceived health is poor, was slightly higher, 7% vs. 5%).

So, this time I calculated perceived health distribution for pure animal phobia and compared it with previous calculations. Code:

Result:

Wrap up

Code all No-s and Unknowns as 0 and treat them equally. This would imply that pure animal phobia is the one, about which we have no evidence that it is combined with any other specific phobias.

#datamangement

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