Black on Blue, Will Not Do: Navigating Canada’s Evidence Based Policing Community as a Black Academic – A Personal Counter-story
Bibliographic record
Abstract
Abstract Purpose – This chapter explores how select “evidence-based” police scholars act as gatekeepers to research opportunities, in Canada, thus impeding critical research that pertains to Black communities. Methodology/Approach – Using the critical race method of counter-storytelling, the following narrative demonstrates how race and racism may play a role in the collection and dissemination of research that examines racial bias in Canadian policing. This methodology aims to refute the notion of critical objectivity, which is often used to promote the principles of evidence-based policing (EBP). Findings – Findings suggest that through various powers and levels within both the policing and academic community, a select number of scholars have influence over Canadian policing research that explores racial bias and discrimination. As such, research that may help to develop effective and efficient policing programs to address racial bias, is thwarted. Originality – No Canadian study explores anti-racist training programs or evaluates their effectiveness. This chapter demonstrates that this may be the result of gatekeeping. The following chapter provides insight into how this is done within EBP circles.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.007 |
| Insufficient payload (model declined to judge) | 0.016 | 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 teacher head, 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".