Anxious solidarities against the mental health crisis: connecting personal struggles to wider social and economic injustices
Bibliographic record
Abstract
Rates of anxiety have been steadily increasing over the past 20 years, prompting commentators to warn that we are in the throes of a global mental health crisis that is ruining well-being, threatening lives and damaging the economy. By highlighting how a person’s mental health, while nuanced and distinct, is always situated in a larger socio-emotional context or ‘structure of feeling’, this article argues that the issue of rising anxiety is a direct consequence of a biomedical model of treatment and care beholden to a neoliberal economic system that objectifies and isolates people. Through a framework termed ‘liberation health modelling’, it explores the progressive potential of ‘anxious solidarities’ as a way to reframe the problem of anxiety by connecting personal struggles to wider social and economic injustices. At a time when it is becoming impossible to deny the collective and widespread nature of people’s anxieties, the point of anxious solidarity is not simply to recount pain and suffering but to ‘make sense’ of it in relation to overarching structures of social oppression – calling into question the status quo in solidarity with other subjugated groups. Since struggles with anxiety have the advantage of being familiar to most, anyone can be a potential provocateur so long as they disavow an entirely personalised framing of their mental health.
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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 | 0.011 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.011 | 0.060 |
| Scholarly communication | 0.015 | 0.013 |
| Open science | 0.002 | 0.017 |
| Research integrity | 0.006 | 0.011 |
| Insufficient payload (model declined to judge) | 0.005 | 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".