Global mental health and its discontents: An inquiry into the making of<i>global</i>and<i>local</i>scale
Bibliographic record
Abstract
Global Mental Health's (GMH) proposition to "scale up" evidence-based mental health care worldwide has sparked a heated debate among transcultural psychiatrists, anthropologists, and GMH proponents; a debate characterized by the polarization of "global" and "local" approaches to the treatment of mental health problems. This article highlights the institutional infrastructures and underlying conceptual assumptions that are invested in the production of the "global" and the "local" as distinct, and seemingly incommensurable, scales. It traces how the conception of mental health as a "global" problem became possible through the emergence of Global Health, the population health metric DALY, and the rise of evidence-based medicine. GMH also advanced a moral argument to act globally emphasizing the notion of humanity grounded in a shared biology and the universality of human rights. However, despite the frequent criticism of GMH promoting the "bio"-medical model, we argue that novel logics have emerged which may be more important for establishing global applicability than arguments made in the name of "nature": the procedural standardization of evidence and the simplification of psychiatric expertise. Critical scholars, on the other hand, argue against GMH in the name of the "local"; a trope that underlines specificity, alterity, and resistance against global claims. These critics draw on the notions of "culture," "colonialism," the "social," and "community" to argue that mental health knowledge is locally contingent. Yet, paying attention to the divergent ways in which both sides conceptualize the "social" and "community" may point to productive spaces for an analysis of GMH beyond the "global/local" divide.
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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 | 0.028 | 0.031 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.015 | 0.206 |
| Scholarly communication | 0.026 | 0.031 |
| Open science | 0.003 | 0.023 |
| Research integrity | 0.014 | 0.015 |
| 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".