Accessing Information on Immigration Detention in Canada
Bibliographic record
Abstract
This chapter examines how the lack of detailed, publicly available information about immigration detention – together with the opacity of the institutions and agencies responsible for detention in Canada – function as sources of social harm. The discussion is situated within the context of what we call a “crisis of transparency” in Canada in relation to accessing carceral sites of detention and information about them. We critically explore the possible role that greater transparency in information about detention and the institutions and agencies responsible for detention can play in reducing social harm and, ultimately, working towards its abolition. The chapter proceeds from the idea that without detailed, publicly available qualitative and quantitative data about immigration detention, it is more challenging to advocate for detained (and detainable) people and their families and communities, push for “evidence-based” policymaking, and argue towards the end of immigration detention. We consider the utility of access to information requests as a method of collecting data about immigration detention in Canada that also contributes to greater transparency and less institutional opacity as a means of reducing social harm. While acknowledging that increased transparency and institutional openness is not a panacea, we argue that it is an important part of humanising the lives lived in detention and making them grievable.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.012 |
| Science and technology studies | 0.016 | 0.005 |
| Scholarly communication | 0.009 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.019 | 0.002 |
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".