Bibliographic record
Abstract
Abstract In the wake of European settler‐colonialism, the indigenous peoples of North America still contend with the social and psychological sequelae of cultural devastation, forced assimilation, social marginality, enduring discrimination, and material poverty within their respective nation‐states. In response to this contemporary legacy of conquest and colonization, a cottage industry devoted to the surveillance and management of the “mental health” problems of Native Americans proliferates in the United States and Canada without abatement. The attention of clinically concerned researchers, practitioners, and policy makers to an indigenous “patient” or “client” base, however, invites critical analysis of the cultural politics of mental health in these contexts. More specifically, the possibility that conventional clinical approaches harbor the ideological danger of implicit Western cultural proselytization has been underappreciated. In this special section of Ethos , three investigators engage the provocative cultural politics of mental health discourse and practice in three diverse Native American communities. Each provides a critical analysis of mental health discourse and practice in their respective research settings, collectively comprising an analytical and political subversion of the potentially totalizing effects of authorized, universalist mental health policy and practice. [mental health, American Indians, psychiatric anthropology, cross‐cultural counseling, postcolonialism]
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.008 | 0.003 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.005 | 0.004 |
| Insufficient payload (model declined to judge) | 0.231 | 0.071 |
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".