Brokering Access Beyond the Border and in the Wild: Comparing Freedom of Information Law and Policy in Canada and the United States
Bibliographic record
Abstract
Contributing to literature on jurisdictional variation in freedom of information (FOI) law and policy, we draw from accounts of experiences of FOI requests submitted to police agencies in nine Canadian provinces and ten US states. We conceptualize these experiences using notions of “brokering access,” “law in the wild,” and “feral law.” Our findings demonstrate key differences in how public police agencies store, prepare, and disclose information at municipal and provincial/state levels in Canada and the US, meaning that FOI‐related feral lawyering in Canada and the United States differs and fluctuates because of the variation in the mode of contact with FOI coordinators, fee estimate practices, and procedures for and responsiveness to appeals. In conclusion, we discuss the implications of our findings for methodological and sociolegal literature about FOI requests and for provincial/state FOI policies in both countries.
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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 | 0.006 | 0.031 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.004 | 0.010 |
| Science and technology studies | 0.030 | 0.019 |
| Scholarly communication | 0.014 | 0.004 |
| Open science | 0.002 | 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".