Distorted Thinking or Distorted Realities? The Social Construction of Anxiety for Women in Neoliberal Late-Stage Capitalism
Bibliographic record
Abstract
Abstract Anxiety disorders are one of the most prevalent mental disorders globally, and 63% of those diagnoses are of women. Although widely acknowledged across health disciplines and news and social media outlets, the majority of attention has left assumptions underlying women's anxiety in the twenty-first century unquestioned. Drawing on my own experiences of anxiety, I will the explore both concept and diagnosis in the Western world. Reflecting on my own experiences through a critical feminist lens, I will investigate the construction of anxiety as mental disorder in the context of neoliberal late-stage capitalism, heteropatriarchy, and biomedical psychiatry. Tracing the postpositivistic foundations of anxiety, as well as the historical and ongoing medicalization and pathologization of women, I will critically consider the sociopolitical implications of constructing anxiety as biomedical disorder. Assumptions underlying mental health will be explored within the context of the construction, experience, and operationalization of gender and the way gender intersects with diverse positionalities, power, knowledge, and neoliberal governance. Weaving the voices of women poets with the biomedical language of disorder, this critical-realist inquiry will explore my anxiety as it relates to the epidemic levels of anxiety among other women, and the late-stage capitalist world within which anxiety flourishes.
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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 | 0.005 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.012 | 0.080 |
| Scholarly communication | 0.012 | 0.007 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.002 | 0.004 |
| 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".