Bibliographic record
Abstract
I begin this project by analyzing the problematic cooption of liberal feminist ideology by corporations, and their prominent representatives, for advertising and branding purposes. While this practice has a long history in the United States, beginning most pronouncedly in the aftermath of the women’s suffrage movement, the threat it presents to grassroots feminism and organized political action on women’s issues has never been greater. I argue that the current neoliberal climate of American society, along with our growing reliance on social media as a platform for political dialogue, has led modern day feminism to an impasse, even while gender inequality is still widely acknowledged and exhaustively discussed. I specifically examine the political efficacy of neoliberal feminisms evoked by Dove’s Real Beauty advertising campaign, Facebook COO Sheryl Sandberg’s Lean In campaign, and the fractured #feminism campaigns of social media. I contrast the political and social ambitions of these neoliberal feminist campaigns with first and second wave feminist activity, along with the work of recent academics. While the three campaigns I examine are unique in authorship, I argue that they are not only philosophically aligned through their common emphasis on individual self-identification and self-improvement, but also demonstrative of a “post-feminist” social climate that undermines organized feminist political activity. In addition, these iterations of neoliberal feminism strongly prioritize the experiences and concerns of middle/upper class, predominantly white women, ignoring the values of inclusion and intersectionality that have become essential to both grassroots and academic feminist discourse in recent years.
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 | 0.004 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.013 | 0.034 |
| Scholarly communication | 0.008 | 0.005 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.006 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".