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Record W3201729532 · doi:10.25071/2563-3694.55

Memeification of Black Women’s Trauma

2021· article· en· W3201729532 on OpenAlexaffvenue
Natalie Stravens

Bibliographic record

VenueNew Sociology Journal of Critical Praxis · 2021
Typearticle
Languageen
FieldSocial Sciences
TopicGender, Feminism, and Media
Canadian institutionsUniversity of Guelph
Fundersnot available
KeywordsHarmTimelineBlack womenSociologyGender studiesCriminologyPolitical scienceHistoryLaw

Abstract

fetched live from OpenAlex

This piece discusses the online and offline discourses on the lives and bodies of Black femme and nonbinary individuals and the harm that is so casually inflicted upon us. Through popular stories of harm performed around famous Black women, such as with rapper Megan Thee Stallion, I connect the history of Black women in popular culture to current online spaces that continue to minimize and trivialize our trauma. I seek to highlight that these stories are not an anomaly, but rather sentiments rooted in the misogynoir that is so entrenched in western culture and have been expanded and weaponized within the online sphere. In addition, the piece challenges the universality of the Black Lives Matter (BLM) movement in its implementation, criticizing its propensity to forget its feminine victims. It is important to emphasize where it has failed and where it needs to be intentional about the people it has overlooked, as this is a movement that began online, where this harm is currently taking place, and at the hands and energies of Black femmes, the very people getting hurt. This piece has manifested from many conversations already occurring in online Black feminist spaces about our treatment and our needs. It invites others into the fold and seeks to encourage individuals to critically reflect on how Black femme and non-binary individuals are presented on their timeline in-between the numerous BLM posts that claim to protect them.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.005
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.196
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.002
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.051
GPT teacher head0.379
Teacher spread0.328 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designQualitative
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations1
Published2021
Admission routes2
Has abstractyes

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