Toward a decolonial psychology: Recentering and reclaiming global marginalized knowledges.
Bibliographic record
Abstract
Because colonialism was tailored to local geographies, any vision of decolonial psychology also needs to address the legacies of particular colonial histories. One of the central questions addressed in this special issue, "Toward a Decolonial Psychology: Recentering and Reclaiming Global Marginalized Knowledges," is what it means to create possible futures that are rooted in Indigenous cultures, place, people, and land and disentangled from colonialism and coloniality. Contributors from different countries and cultural contexts provide readers with a useful mapping of the granular, place-based decolonial research that has flourished in psychology over the past 10 years. We foreground scholarship that speaks to the lives of the majority world and to those whose lives continue to be marginalized within settler-colonial states such as the United States, Canada, and New Zealand. We highlight six distinct but related overarching themes that focus on retrieval and reclamation of global marginalized knowledges: (a) psychology's colonial past and present; (b) transnational decoloniality: beyond the binary of Global North and South; (c) race, racism, and colonial domination: psychology, materiality, and identity; (d) beyond individualism: community, relational agency, and collective liberation in decolonial psychology; (e) settler colonialism and Indigenous psychologies: reclamation of land, culture, spirituality, and ecology; (f) decolonizing psychological health: beyond resilience, neurocolonization, and biomedical approaches to well-being. (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 | 0.008 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.011 | 0.068 |
| Scholarly communication | 0.016 | 0.023 |
| Open science | 0.002 | 0.012 |
| Research integrity | 0.004 | 0.011 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".