Mark of the Non-Conviction Record: Managing Stigma and Confronting the Consequences of Recorded Police Contact in Canada
Bibliographic record
Abstract
This dissertation investigates the experiences of people with non-conviction records (NCR) in Canada and how they contend with the stigma of this police record type. Minimal sociological and criminological research focuses on NCR holder experiences despite tens of thousands of NCRs being generated in Canada annually, and NGOs and legal scholars reporting that NCR exposure has similar stigma-induced repercussions to disclosing a conviction record (CR). I address this literature gap by investigating whether, why, and how recorded police contact leads to challenges for NCR holders, and how NCR holders address record-related repercussions. I draw on interviews with twenty-two NCR holders and twelve key informants across Ontario to understand NCR holder perceptions of NCR consequences, NCR holder strategies for contending with record stigma, and how NCR holder experiences compare to those of CR holders in other studies. I argue that the mark of an NCR can affect NCR holders in similar ways to CR holders, as indicated by the NCR-induced collateral consequences they experience, the record-induced discrimination they anticipate, and their use of strategies to prevent stigmatization. Interviews revealed diverse NCR exposure experiences and repercussions, including social, professional, and economic marginalization. All NCR holder participants anticipated, and most experienced, consequences in at least one area of their lives, including employment, financial security, volunteering, career training, U.S. travel, personal relationships, and sense of well-being. Collateral consequences resulted from NCRs appearing on security checks as well as NCR holders holding back from opportunities due to their perceived risk of record exposure and stigma-induced rejection. NCR holders used stigma avoidance tactics to retreat from opportunities with exposure and rejection potential; alternatively, they used stigma management strategies to resist consequences through impression management and information control. Strategy choice was primarily related to perceptions of when and where NCR consequences may occur. The findings suggest Canadians are not guaranteed the right to be considered “innocent until proven guilty” since the mere mark of police contact can have long-term detrimental effects on people who were never convicted of a crime. Overall, this dissertation contributes to broader discussions about criminal record stigma and record disclosure reform.
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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 | 0.005 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.036 | 0.011 |
| Scholarly communication | 0.008 | 0.003 |
| Open science | 0.003 | 0.008 |
| Research integrity | 0.002 | 0.003 |
| 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".