MISOGYNY, SURVIVORSHIP, AND BELIEVABILITY ON DIGITAL PLATFORMS: EMERGING TECHNIQUES OF ABUSE, RADICALIZATION, AND RESISTANCE
Bibliographic record
Abstract
On 18th May 2022, in an opinion piece for The New York Times, columnist Michelle Goldberg declared “the death of #MeToo” (Goldberg, 2022). The papers in this panel examine this claim and wrestle with its potential implications. Drawing on case studies and data from the United States, Australia, the United Kingdom, and Ireland, we evaluate the current state of play in the online push-and-pull between feminist speech about gender-based violence and its attendant misogynistic backlashes. Using a range of different qualitative methods, these papers unpack the orientations towards visibility and transparency that urge survivors into ever-increasing degrees of exposure online; the way that digital media are reconfiguring the gender and racial politics of doubt and believability; the algorithmic pathways through which boys and men are ushered towards increasingly more radical “manosphere” content and communities; and how the problem of “believability” as it relates to testimonies of assault is being complicated and compounded online by networked misogynoir. The result is an ambivalent portrait of the afterlife of #MeToo on the internet, and some important questions for networked feminist activism going forward.
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 | 0.014 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.013 | 0.083 |
| Scholarly communication | 0.017 | 0.021 |
| Open science | 0.002 | 0.013 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.006 | 0.000 |
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.
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".