Bibliographic record
Abstract
Re-entry for formerly incarcerated Black people in Canada has become an urgent social and political issue over the past decade, especially in Toronto. From 2020 to 2021, 9% of people under federal jurisdiction, whether in custody or being supervised in the community, were Black, despite only consisting of about 4% of Canadian adults (OCI, 2022). As such, the Black narrative surrounding lived experience is invaluable. Albeit at times viewed as homogenous in nature, the Black experience is truly one of a kind. So much diversity lies in the community in which we find ourselves co-existing. To define lived experience means capturing the tones of diversity , priority and collaboration . Lived experiences in the Black community are varied but can be categorized according to personal and vicarious experiences. Personal experience can be defined as an individual having a firsthand account of being incarcerated. Conversely, vicarious experiences are relational, whereby one&s;s lived experience is based on secondhand accounts or observations of the impacts of the criminal justice system. At times, vicarious lived experience is more than just a point of reference for storytelling. Rather, there is, at times, a physiological and psychological trauma response that is elicited when someone sees or hears of the repeated impacts of the criminal justice system. Priority means stepping back when shining light on personal lived experience . At times, it is tempting for academics with no personal lived experience to over-participate in the narrative about incarceration. As a reflexive scholar, I am mindful of this tendency. As such, we use the term priority to place value on the lived-experiences that are most enriching to the conversation, which is personal lived experience . Collaboration captures the fact that these two voices co-exist in space to provide a comprehensive understanding of lived experience . Co-authors Kadija Osei and Christopher Husbands are living examples of how we define vicarious and personal lived experience , respectively.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.892 | 0.632 |
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".