Bibliographic record
Abstract
Decolonizing may be conceptualized through the interconnected processes of deconstructing colonial ideologies and their manifestations, and reconstructing colonial discourse through Indigenous counter'narratives. Given that the field of psychology is firmly rooted in colonial systems of thought, it is integral that professionals in psychology and allied disciplines engage in meaningful, beneficial work with Indigenous communities through actively decolonizing and Indigenizing research, practice, and education. This paper illustrates an embodied approach to decoloniality through Indigenizing curriculum and pedagogy in community psychology and allied fields. Drawing on both Indigenous research and experience, the author presents a framework for decolonizing and Indigenizing curriculum through: (a) deconstructing what is not working in service provision with Indigenous communities; (b) restor(y)ing colonial narratives through community'based Indigenous perspectives that highlight the importance of love, good relationships, Indigenous knowledge, local approaches to wellness, responsibility, identity/belonging, and the land/earth in community wellness; and (c) how Indigenous best practices may be engaged through community'based processes and transformations. The author then discusses how Indigenous pedagogies may be enacted using Indigenous protocols and ethics, talking circles, storytelling, and land'based pedagogies. The paper concludes with the author's reflections on the challenges and rewards of decolonizing and Indigenizing in conventional postsecondary educational systems.
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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 | 0.010 |
| 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 | 0.034 |
| Scholarly communication | 0.008 | 0.007 |
| Open science | 0.001 | 0.016 |
| Research integrity | 0.002 | 0.004 |
| 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".