Decolonizing mental health in the polycrisis: Pathways toward neuro-decolonization.
Bibliographic record
Abstract
The convergence of ecological, social, economic, and psychological crises into a "polycrisis" endangers the well-being of all life on Earth. This article argues that the root causes and drivers of these interconnected crises lie within the modern/colonial system, particularly its imposed illusion of separation between humanity and the rest of nature. Drawing on the work of Yellow Bird and our collaborations with Indigenous communities in Brazil and Peru, we examine how neurocolonization-the systemic imprinting of separability, superiority, and subjugation onto ways of thinking, perceiving, relating, feeling, and being-has contributed to the polycrisis. By comparing two mental health paradigms, one based on separability and the other on entanglement, we suggest that neuro-decolonization would entail a dual process by which those socialized into separability unlearn harmful patterns and unnumb to their relational interdependence and responsibility toward all beings, while those socialized into entanglement reclaim and revitalize practices that have been suppressed and pathologized. We also offer possible starting points for the field of psychology to address its role in neurocolonization and support neuro-decolonization in an era of polycrisis. (PsycInfo Database Record (c) 2025 APA, all rights reserved).
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.037 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.001 | 0.005 |
| 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".