An Exploratory Descriptive Study of Implicit Bias in Police Recruits and Applicants at the RCMP
Bibliographic record
Abstract
Abstract An Exploratory Descriptive Study of Implicit Bias in Police Recruits and Applicants at the RCMP Philip Elias Semple Doctor of Philosophy Department of Leadership, Higher and Adult Education, Ontario Institute for Studies in Education University of Toronto 2022 This study sought to explore the nature, degree and impact of implicit bias in police recruits and their instructors attending the Royal Canadian Mounted Police (RCMP) training facility in Regina, Saskatchewan. The bias explored was in relation to Black men and women. Findings were generated using a mixed methods form of research. Quantitative data were obtained using an implicit bias measuring tool and qualitative data were gleaned from key experts external to the police services. The rationale behind choosing the implicit bias test called Implicitly as the main data collection instrument, its characteristics, and established validity are discussed. The issues of bias and racism are explored from a police and social perspective. Implications, ways to mitigate implicit bias and prevent unsuitable candidates from being hired into positions of power such as policing are also discussed. Significant limitations were encountered in the implementation of the initial research protocol. At the original intended test site, the Ontario Police College (OPC), none of the instructors and only two recruits attempted to complete the Implicitly test. Furthermore, at the second site, the RCMP Depot, none of the instructors chose to participate and only 61 out of 800 invited recruits completed the Implicitly test. However, the Implicitly tests were subsequently offered by the RCMP to recruit applicants and a robust sample of 5,282 tests were completed and the aggregated results made available to me for this study as de-identified secondary data. These secondary data provided the focus of the discussions with five key external experts who shared their perceptions stressing the need for the testing to be a mandatory part of the hiring process for policing because of the potential impact of bias in officer’s position of power. They also thought that the implicit bias testing could be useful in helping to determine the efficacy of recruit training. Key findings identified some significant limitations. These overall test results showed low ranges of implicit bias for both RCMP recruits who completed the tests and recruit applicant participants. Exploring the reason(s) for the lack of participation by the instructors and recruits urgently warrants further investigation.
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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 | 0.011 | 0.036 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.006 | 0.004 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| 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".