Toward Anti-Racism in Canadian Psychology: A Call to Action from the Future of the Field
Bibliographic record
Abstract
Racism exists at the individual and systemic level where power differentials and hierarchies oppress racial minorities. Canada’s auspicious history of colonialism and systemic racism puts Black, Indigenous, and People of Colour at-risk of poor psychological health, in addition to a host of other health-related concerns. As psychologists and trainees, our ethical duty, as outlined in our Code of Ethics, includes the responsibility to society. Recent sociocultural discussions and political movements (e.g., Black Lives Matter) highlight the need to confront and address issues of long-standing racism and inequity. As such, this commentary provides a brief review of the current and historical contexts of racism in psychology and highlights the need for a concrete, actionable plan to address these concerns. Consequently, we propose 18 recommendations to address the reviewed problems. We believe that these recommendations will help various settings that house and train psychologists as well as ones that regulate the profession (e.g., academic psychology departments, hospitals, private practices, governing bodies for psychology) to adopt anti-racist measures that provide a more inclusive environment.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.031 | 0.038 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.046 | 0.049 |
| Scholarly communication | 0.025 | 0.011 |
| Open science | 0.005 | 0.008 |
| Research integrity | 0.017 | 0.028 |
| Insufficient payload (model declined to judge) | 0.006 | 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".