Outsiders: The Sources and Impact of Secrecy at the Iacobucci Inquiry
Bibliographic record
Abstract
The Internal Inquiry into the Actions of Canadian officials in relation to Abdullah Almalki, Ahmad Abou-Elmatti and Muayyed Nureddin is a particularly pronounced example of the use of secrecy that has defined Canada in the wake of 9/11. Despite having the authority to hold some portions of the Inquiry in public, the Iacobucci Inquiry was conducted almost exclusively in camera and ex parte. The result was an inquiry that was unlike previous commissions called under the federal Inquiries Act. Taking the perspective of those outside the process, the author explores the question of what makes a commission of inquiry successful by revisiting the functions and objectives of public inquiries in Canada, in an analysis that invokes the exclusion and marginalization experienced by the three men. Relying on both case law and scholarship, the author proposes an analytic framework by which to assess commissions of inquiry and the textent to which they achieve their traditional objectives: information and education; restorative justice; and socio-democratic functions. Applying criteria derived from the literature on new governance to the internal inquiry model, the author concludes that inquiries conducted in secret fall significantly short of attaining their objectives.
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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 | 0.020 | 0.041 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.036 | 0.081 |
| Scholarly communication | 0.035 | 0.007 |
| Open science | 0.002 | 0.013 |
| Research integrity | 0.004 | 0.007 |
| 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".