Bibliographic record
Abstract
In a patriarchal world where misogyny is rampant and seemingly on the rise, just being female is enough to be relegated to otherness status. But what happens when additional stigmas, such as gender nonconformity or fatness, are layered onto an already marginalized female identity? The outcome is a form of intersectional experience where various types of discrimination interact and create an even greater level of prejudice. Furthermore, when analysts and patients share experiences of intersectional shame that confer non-privilege—like being female and fat—the shame residing in both can lead to significant disruption between them. Reflecting on my own countertransferential experience of non-privilege made it hard for me to see how my patient, who identified as female, fat, and transgender, might be suffering even more as a result of her compounded identities. Given the countless ways that intersectional identities can intersect, we can all gain from sharing clinical narratives that help illuminate these complexities.
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 | 0.005 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.015 | 0.073 |
| Scholarly communication | 0.009 | 0.011 |
| Open science | 0.001 | 0.016 |
| Research integrity | 0.003 | 0.009 |
| Insufficient payload (model declined to judge) | 0.003 | 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".