Beyond Adaptation: Decolonizing Approaches to Coping With Oppression
Bibliographic record
Abstract
How should one respond to racial oppression? Conventional prescriptions of mainstream social psychological science emphasize the idea of<italic>coping</italic>with oppression—whether via emotional management strategies that emphasize denial or disengagement; problem-focused strategies that emphasize compensation, self-efficacy, or skills training; or collective strategies that emphasize emotional support—in ways that promote adaptation to, rather than transformation of, oppressive social structures. Following a brief review of the literature on coping with racism and oppression, we present an alternative model rooted in perspectives of liberation psychology (Martín-Baró, 1994). This decolonial approach emphasizes critical consciousness (rather than cultivated ignorance) of racial oppression, a focus on de-ideologization (rather than legitimation) of status quo realities, and illumination of models of identification conducive to collective action. Whereas the standard approach to coping with oppression may ultimately both reinforce and reproduce systems of domination, we propose a decolonial approach to racism perception as a more effective strategy for enduring prosperity and well-being.
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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 | 0.006 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.043 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.002 | 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".