Bibliographic record
Abstract
Misogyny is a weighty term. Its affective power invokes spectres of rape, sexual assault, hate-fuelled insults and gas-lighting. Its presence in nearly every culture on the planet haunts our pasts and frames our presents. Aiming to build an understanding of misogyny for our future social justice efforts, I look to Kate Manne’s Down Girl: The Logic of Misogyny, where she dusts off an old definition of misogyny as the hatred of women to describe it as the enforcement branch of a patriarchal society, a renewed engagement for feminists and activists alike. In particular, this framing provides opportunities to examine misogyny from an intersectional lens, including its intersections with race, gender and sexuality. For example, through stories such as that of Pamela George, an Indigenous woman from Regina, Saskatchewan who was murdered in 1995, I argue that it is crucial that we recognise the collusion between settler colonialism and misogyny. Or in the case of transphobic comedian Dave Chapelle, we must understand the interplay of heteronormativity and cisnormativity in propping up transmisogyny. Consequently, I argue that an intersectional logic of misogyny provides not only a shift but a tipping point for feminist and queer movements to come.
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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.010 | 0.024 |
| Scholarly communication | 0.009 | 0.013 |
| Open science | 0.001 | 0.009 |
| Research integrity | 0.006 | 0.007 |
| Insufficient payload (model declined to judge) | 0.017 | 0.005 |
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".