Bibliographic record
Abstract
Women are at least twice as likely to experience depression as men, and up to 25% of women can expect to be depressed in their lifetimes. Depression is likely to recur in up to 85% of women, yet most women who experience depression cope on their own. Feminist research has explored the discursive, and social context of depression among women and acknowledges women’s agency as they simultaneously struggle and cope with depression. “Getting on with life” is often an imperative, but begs the question what are they getting on with, especially if their lives have been significant in causing unhappiness and distress. I explore how depression is shaped by the discourse of self-management, gender performance and the notion “the good woman.” Dominant depression discourses individualize, decontextualize, and emphasize personal responsibilization for the causes and treatment of depression. This produces an epistemic injustice for speaking about and coping with depression. Social work practitioners must make space for acknowledging women’s resourcefulness and agency in their management of sadness and distress. We must also address not only the dangers of responsibilization, but the limitations of this approach to women’s well-being.
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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.012 | 0.032 |
| Scholarly communication | 0.011 | 0.005 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 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".