Bibliographic record
Abstract
Abstract Racism is a pervasive problem in Western society, leading to mental and physical unwellness in people from racialized groups. Psychology began as a racist discipline and still is. As such, most clinical training and curricula do not operate from an anti-racist framework. Although most therapists have seen clients with stress and trauma due to racialization, very few were taught how to assess or treat it. Furthermore, clinicians and researchers can cause harm when they rely on White-dominant cultural norms that do not serve people of colour well. This paper discusses how clinicians can recognize and embrace an anti-racism approach in practice, research, and life in general. Included is a discussion of recent research on racial microaggressions, the difference between being a racial justice ally and racial justice saviour, and new research on what racial allyship entails. Ultimately, the anti-racist clinician will achieve a level of competency that promotes safety and prevents harm coming to those they desire to help, and they will be an active force in bringing change to those systems that propagate emotional harm in the form of racism. Key learning aims (1) Knowledge of how racism manifests in therapy, psychology and society. (2) Understanding the difference between racial justice allyship versus saviourship. (3) Increased awareness of microaggressions in therapy. (4) Appreciation of the importance of combatting systemic racism.
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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 | 0.007 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.011 | 0.007 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.036 | 0.006 |
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".