Truth and reconciliation for whom? Transitional justice for Indigenous peoples in American psychology.
Bibliographic record
Abstract
In October 2021, the American Psychological Association apologized to people of color in the United States for its role in systemic racism. Spurred by a national racial reckoning, Indigenous Peoples have been regularly incorporated into initiatives redressing America's legacy of racism. Although Indigenous Peoples have been racialized during the formation of the United States, this process is intertwined with colonization-the systematic dispossession and exploitation of Indigenous communities by Europeans. We first examine how the American Psychological Association (APA) has been complicit in colonialism by failing to oppose government policies that disenfranchise Indigenous communities, which it recently recognized in a separate apology to First Peoples in the United States in February 2023 (American Psychological Association, APA Indigenous Apology Work Group [APA IAWG], 2023). Second, we explore methods for APA to reconcile historical and contemporary wrongs inflicted on Indigenous Peoples through transitional justice, an approach to addressing human rights violations that seeks justice and opportunities for healing (United Nations, 2008). In particular, we consider the implications that Truth and Reconciliation Commissions have for Indigenous Peoples. Third, we provide recommendations for APA to repair relations with Indigenous Peoples in education, research, and practice. We specifically interrogate what possibilities for truth, reconciliation, and healing exist vis-à-vis transitional justice in psychology. We conclude with the potential that APA has to advance meaningful structural reforms while cautioning against superficial efforts towards reconciliation. (PsycInfo Database Record (c) 2024 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 | 0.009 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.028 | 0.026 |
| Scholarly communication | 0.009 | 0.013 |
| Open science | 0.001 | 0.009 |
| Research integrity | 0.005 | 0.014 |
| 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".