Bibliographic record
Abstract
EN-LIGHTEN-ED: The politics of Black hair is a blog post that spotlights issues which primarily affect women in their daily lives with a focus on creating awareness and encouraging fellow young girls like myself, with a platform to use their voices. For the final media project of my COMS 479 Feminist Media Studies class, I precisely administered the blog post to engage in a conversation on the politics of Black women’s hair. Through the medium of my blog, I was opportune to implore a fundamental principle of Feminist Media Studies, that of Representation, with a concentration on the representations of otherness – which in this case were Black women. This blog post took a critical standpoint to dissect and disintegrate the negative portrayals of Black femininity that have slowly become internalized by a majority of society. The topic of hair is downrightly a focus when femininity is the subject of discussion. On this account, and with aid of my media piece, I was then able to propose burdening questions and seek answers as to why Black women were steadily weighed down with the expectancy to live up to the standards of Eurocentrism through the manner in which they decide to wear their hair in. This submission is a creative webpage. To view the site Click Here.
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 machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.466 | 0.131 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".