Bibliographic record
Abstract
To evaluate the performance of FOI regimes, associations of journalists and other groups undertake FOI audits. These audits assess the depth of disclosure, the use of exemptions, among other indicators of the health of FOI laws. Drawing on a thematic analysis of FOI audits, we examine how these audits are conducted and what the audits reveal about FOI in multiple jurisdictions. We discern four themes in these audits: (1) law enforcement and security hindrance of FOI, (2) a link between FOI advocacy and struggles for government transparency, (3) gross abuses of FOI, and (4) the potential for social change. Arguing that FOI audits are a form of access advocacy, we suggest future FOI audits could be more community-based and participatory. We also provide recommendations for those undertaking future FOI audits.
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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 | 0.159 | 0.349 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.015 | 0.015 |
| Science and technology studies | 0.010 | 0.019 |
| Scholarly communication | 0.017 | 0.024 |
| Open science | 0.002 | 0.018 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".