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Author Archives: Alison Boldero

Skin Deep at NYFW (Group Post #8)

 

Last week, our group met with Alycia Sellie at the GC Library to discuss copyright, fair use, and the runway photos we collected from Vogue.com for our project.  Although there are concerns to consider, we’ve come to terms with there being no easy answer and decided to move forward with developing our visualizations to supplement the analytical narrative threads we had presented in our last class session (missing data/ messy data, tokenism, and racial categorization).  From the resources provided to us from librarians, it is clear that we must make an effort to “transform” the images we use for educational purposes in order to “make meaning” and illustrate our observations.

If anyone is interested in those resources on copyright and fair use, please let us know and we could forward you what we have.  We also intend to preserve our project in a traditional essay format.

Skin Deep at NYFW (FKA Charting Diversity at NYFW, GP #5)

ETA: We also have a temporary landing page up on WordPress:

Skin Deep at NYFW


We are happy to announce a group name and the creation of our Twitter! Please follow us at:

@skindeepNYFW

Last week, we were able to attend the “People Centered Digital Research at the GC” workshop hosted by GC Digital Fellows Jennifer Tang and Patrick Sweeney, who spoke about how using digital tools or digital methods either enhances meanings or changes the way we make meanings in our collection of data.

Discussions with the GC Fellows have helped us work through conceptualizing our project as well as navigate issues of race using available readings.  From one such reading, “Managing the Semiotics of Skin Tone: Race and Aesthetic Labor in the Fashion Modeling Industry” by Elizabeth Wissinger, the author indicates that existing public data on models is few and far between and what has been made available by the U.S. Bureau of Labor Statistics does not provide information on race or gender.  This is one obstacle our group faces and we’ve set ourselves a project deadline in the event that we’ll need to switch gears.  Still, there are many avenues to explore regarding the presentation and performance of race on the runway at NYFW and we are prepared to do what we can in order to tell this story.

Diversity in NYFW (Group Post #2)

During last Week 4’s class session, our group presented to the class our project’s premise, team, and tentative project phases. We are grateful to have met with GC Digital Fellow Jennifer Tang during class, whose questions and observations regarding our project presentation helped us begin to establish more deliberate steps in collecting initial data and better articulate our project’s critical direction.

Before we can begin to analyze model data, we had to create a strategy for selecting the designers who employed these models according to how often a designer was covered during the most recent New York Fashion Week that occurred between February 11 – 18, 2016, by fashion magazines carrying weight in the industry. Between the three of us, we split up nine fashion magazines and created a To-do list on Basecamp to verify our progress. Designers covered have been collected on a Google spreadsheet inside of a shared Google folder on Google Drive.

Shoutout to Nico for making contact with a primary source for past NYFW data and to Scarlett for organizing a meeting with a group member from the New York City Fashion Index (NYCFI).

Alison Boldero

 

Data Presentation: Content Analysis and “In the Country”

Officially, my data set project is an attempt at content analysis using a short story collection as my chosen data set. In reality, this was me taking apart a good book so I could fool around with Python and MALLET, both of which I am very new to. In my previous post, I indicated that I was interested in “what the investigation of cultural layers in a novel can reveal about the narrative, or, in the case of my possible data set, In the Country: Stories by Mia Alvar, a shared narrative among a collection of short stories, each dealing specifically with transnational Filipino characters, their unique circumstances, and the historical contexts surrounding these narratives.” I’ve begun to scratch at the surface.

I prepared my data set by downloading the Kindle file onto my machine. This presented my first obstacle: converting the protected Kindle file into something readable. Using Calibre and some tutorials, I managed to remove the DRM and convert the file from Amazon’s .azw to .txt. I stored this .txt file and a .py file I found on a tutorial for performing content analysis using Python under the same directory and started with identifying a keyword in context (KWIC). After opening Terminal on my macbook, I typed the following script into the command line:

python kwic1.py itc_book.txt home 3

This reads my book’s text file and prints all instances of the word “home” and three words on either sides into the shell. The abbreviated output from the entire book can be seen below:

Alisons-Air:~ Alison$ ls
Applications Directory Library PYScripts Test
Calibre Library Documents Movies Pictures mallet-2.0.8RC2
Desktop Downloads Music Public
Alisons-Air:~ Alison$ cd PYScripts/
Alisons-Air:PYScripts Alison$ ls
In the Country
Alisons-Air:PYScripts Alison$ cd In\ the\ Country/
Alisons-Air:In the Country Alison$ ls
itc_book.txt itc_ch1.txt itc_ch2.txt kwic1.py twtest.py
Alisons-Air:In the Country Alison$ python kwic1.py itc_book.txt home 3
or tuition back [home,] I sent what
my pasalubong, or [homecoming] gifts: handheld digital
hard and missed [home] but didn’t complain,
that I’d come [home.] What did I
by the tidy [home] I kept. “Is
copy each other’s [homework] or make faces
my cheek. “You’re [home,”] she said. “All
Immaculate Conception Funeral [Home,] the mortician curved
and fourth days [home;] one to me.
was stunned. Back [home] in the Philippines
farmer could come [home] every day and
looked around my [home] at the life
them away back [home,] but used up
ever had back [home—and] meeting Minnie felt
shared neither a [hometown] nor a dialect.
sent her wages [home] to a sick
while you bring [home] the bacon.” Ed
bring my work [home.] Ed didn’t mind.
“Make yourself at [home,”] I said. “I’m
when Ed came [home.] By the time
have driven Minnie [home] before, back when
night Ed came [home] angry, having suffered
coffee in the [homes] of foreigners before.
of her employer’s [home] in Riffa. She
fly her body [home] for burial. Eleven
of their employers’ [homes] were dismissed for
contract. Six went [home] to the Philippines.
the people back [home,] but also: what
she herself left [home.] “She loved all
I drove her [home,] and then myself.
we brought boys [home] for the night.
hopefuls felt like [home.] I showed one
She once brought [home] a brown man
time she brought [home] a white man
against me back [home] worked in my
the guests went [home] and the women
I’d been sent [home] with a cancellation
feed,” relatives back [home] in the Philippines
we’d built back [home,] spent our days
keep us at [home.] Other women had
Alisons-Air:In the Country Alison$

I chose the word “home” without much thought, but the output reveals an interesting pattern: back home, come home, bring home. Although this initial analysis is simple and crude, I was excited to see the script work and that the output could suggest that the book’s characters do focus on returning to the homeland or are preoccupied, at least subconsciously, with being at home, memories of home, or matters of the home. In most of In the Country’s chapters, characters are abroad as Overseas Filipino Workers (OFWs). Although home exists elsewhere, identities and communities are created on a transnational scale.

Following an online MALLET tutorial for topic modeling, I ran MALLET using the command line and prepared my data by importing the same .txt file in a readable .mallet file. Navigating back into the MALLET directory, I type the following command:

bin/mallet train-topics --input itc_book.mallet

— And received the following abbreviated output:

Last login: Sun Nov 29 22:40:08 on ttys001
Alisons-Air:~ Alison$ cd mallet-2.0.8RC2/
Alisons-Air:mallet-2.0.8RC2 Alison$ bin/mallet train-topics --input itc_book.mallet
Mallet LDA: 10 topics, 4 topic bits, 1111 topic mask
Data loaded.
max tokens: 49172
total tokens: 49172
LL/token: -9.8894
LL/token: -9.74603
LL/token: -9.68895
LL/token: -9.658470 0.5 girl room voice hair thought mother’s story shoulder left turn real blood minnie ago annelise sick wondered rose today sit
1 0.5 didn’t people work asked kind woman aroush place hospital world doesn’t friends body american began you’ve hadn’t set front vivi
2 0.5 back mother time house can’t you’re home husband thought we’d table passed billy family hear sat food stop pepe radio
3 0.5 day i’d made called school turned mansour manila don’t child things jackie mouth wasn’t i’ll car air boy watch thinking
4 0.5 hands years water morning mother head girl’s sound doctor felt sabine talk case dinner sleep told trouble books town asleep
5 0.5 he’d life man bed days found inside husband country call skin job reached wrote york past mind philippines chair family
6 0.5 time knew looked it’s she’d girls felt living i’m floor president fingers jim’s john young church jorge boys women nurses
7 0.5 baby hand city jaime door words annelise andoy heard he’s gave put lived that’s make white ligaya held brother end
8 0.5 milagros night face couldn’t year son brought men head money open they’d worked stood laughed met find eat white wrong
9 0.5 jim father home children eyes mrs milagros told long good years left wanted feet delacruz she’s started side girl streetLL/token: -9.62373
LL/token: -9.60831
LL/token: -9.60397
LL/token: -9.60104
LL/token: -9.596280 0.5 voice room you’re wife mother’s he’s story wrote closed walls stories america father’s ago line times sick rose thought today
1 0.5 didn’t people asked kind woman place hospital work city body doesn’t started front milagros american you’ve hadn’t held set watched
2 0.5 mother back house school thought can’t days bed minnie parents billy we’d table passed read sat stop high food they’re
3 0.5 day i’d made manila called don’t turned mansour child head hair jackie mouth dark wasn’t car stopped boy watch bedroom
4 0.5 man hands morning water reached doctor real sabine dinner sleep town asleep isn’t told dead letters loved slept press standing
5 0.5 husband he’d life family found inside call country skin live past daughter book mind chair wall heart window shoes true
6 0.5 time it’s knew looked felt she’d living i’m floor close president fingers things young began church boys women thing leave
7 0.5 baby hand jaime annelise door room words andoy hear heard lived put brother make that’s paper ligaya city end world
8 0.5 milagros night face couldn’t white son year brought men work job open stood they’d met money worked laughed find head
9 0.5 jim girl home years father children eyes aroush left good long mrs told she’s wanted girls love gave feet girl’sLL/token: -9.59296

LL/token: -9.59174

Total time: 6 seconds
Alisons-Air:mallet-2.0.8RC2 Alison$

It doesn’t make much sense, but I would consider this a small success only because I managed to run MALLET and read the file. I would need to work further with my .txt file’s content for better results. At the very least, this MALLET output could also be used to identify possible categories and category members for dictionary-based content analysis.

Workshop: The GC Python Users’ Group (PUG)

Last week, I attended my first Python Users’ Group (PUG) with the GC Digital Fellows for help with setting up and accessing Python on my machine.  They meet every other Monday from 2 – 4PM in the GC Digital Scholarship Lab (Room 7414) to help and support students working on digital projects.  So far this semester, I’ve attended several workshops but felt that the instruction I received at PUG (on both Python and MALLET) was particularly helpful due to the collaborative atmosphere and the opportunity for one-on-one discussions.

Evidence of my attendance: me on the far right, confused but determined.

Evidence of my attendance: me on the far right, confused but determined.

PUG will meet again next week, November 16.  A recap from last week’s session makes two suggestions for participants:

  1. Come to learn Python independently with a group of tutors nearby.
  2. Come with a specific Python-related question or project that you want help with.

I regret not having a data set prepared to work on the last time I attended but I encourage everyone in class to go with something to work on.  Since the last session, I have been working on accessing my data set (a recently published short story collection) and converting it into a readable file for analysis.

Data Project: Reading Transnationalism and Mapping “In the Country”

Last week, we discussed “thick mapping” in class using the Todd Presner readings from HyperCities: Thick Mapping in the Digital Humanities, segueing briefly into the topic of cultural production and power within transnational and postcolonial studies (Presner 52). I am interested in what the investigation of cultural layers in a novel can reveal about the narrative, or, in the case of my possible data set, In the Country: Stories by Mia Alvar, a shared narrative among a collection of short stories, each dealing specifically with transnational Filipino characters, their unique circumstances, and the historical contexts surrounding these narratives.

In the Country contains stories of Filipinos in the Philippines, the U.S., and the Middle East, some characters traveling across the world and coming back. For many Overseas Filipino Workers (OFWs), the expectation when working abroad is that you will return home permanently upon the end of a work contract or retirement. But the reality is that many Filipinos become citizens of and start families in the countries that they migrate to, sending home remittances or money transfers and only returning to the Philippines when it is affordable. The creation of communities and identities within the vast Filipino diaspora is a historical narrative worth examining and has been a driving force behind my research.

For my data set project, I hope to begin by looking at two or more chapters from In the Country and comparing themes and structures using Python and/or MALLET. The transnational aspect of these short stories, which take place in locations that span the globe, adds another possible layer of spatial analysis that could be explored using a mapping tool such as Neatline. My current task is creating the data set – if I need to convert it, I could possibly use Calibre.

Difficult Thinking, Cultural Criticism, and Niceness in DH

Suggested Reading and a Summer Institute

In “Difficult Thinking about the Digital Humanities” from last week’s reading, Mark Sample discusses critical thinking in comparison to facile thinking and how accounts of facile thinking “eliminate complexity by leaving out history, ignoring examples, and – in extreme examples – insisting that any other discourse about the digital humanities is invalid because it fails to take into consideration that particular account’s perspective.”  He references Alan Liu’s call for more cultural criticism in DH as an example of similar initiatives.  Liu’s call for more cultural criticism in DH seems more of a side note in our recent readings, including this week’s “Digital Humanities and the ‘Ugly Stepchildren’ of American Higher Education” by Luke Waltzer.  I would’ve liked to read more on the subject of DH criticism outside of the methodology conversation, as described below in articles by Alan Liu and Adeline Koh:

Rough stuff.

I also have below another Koh article that could be read in conjunction with Tom Scheinfeldt’s “Why Digital Humanities Is ‘Nice’” and Lisa Spiro’s “‘This Is Why We Fight’: Defining the Values of the Digital Humanities.”  Koh focuses her argument on the neutrality of “niceness” and the exclusionary nature of more “hack” than “yack,” articulating my personal anxieties regarding the social and technical requirements of DH.

On additions to the syllabus for today’s DH Pedagogy topic, I suggest taking a look at the Humanities Intensive Learning & Teaching Institute, or HILT2015, an annual summer institute that provided workshops on digital pedagogy and criticism with courses such as “Getting Started with Data, Tools, and Platforms” and “De/Post/Colonial Digital Humanities.”