Sustaining Systemic Racism Through Psychological Gaslighting: Denials of Racial Profiling and Justifications of Carding by Police Utilizing Local News Media
Bibliographic record
Abstract
This article examines Police Services and local media discourses on street checks in Hamilton, Ontario, from June 2015 to April 2016 and their usage as a form of psychological abuse known as gaslighting. Despite the widespread coverage that the Hamilton Police Service received as a result of being linked to systemic racist practices, a year later, the Hamilton Police Service was able to avoid being implicated in deliberately conducting racial profiling through strategic tactics in the discourse they relied upon and presented in the media. Through an analysis of 27 local news media articles on the topic of street checks, it is argued that the Police Services and local media discourse enact gaslighting, a form of psychological abuse that is used to manipulate object(s) in order to deceive and undermine the credibility of the target. The psychological effects of gaslighting on people of color included a sense of alienation, disenfranchisement from the community, and distrust toward the police. Through a case study application, it is suggested that gaslighting is part of a systemic, historical process of racism that has been used by the police and government organizations to both illegally target people of color and deny complicity in racial profiling.
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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 | 0.003 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.012 | 0.021 |
| Scholarly communication | 0.008 | 0.004 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.002 | 0.003 |
| 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".