Bibliographic record
Abstract
[extract] Nations across the globe have enacted government transparency laws, potentially enabling people to learn what those governments are “up to.” Such laws require governments to provide official documents upon request, albeit often with major exceptions allowing the government to withhold or redact many documents. Scholars have devoted much attention to analysis and assessment of these exceptions and the enforcement mechanisms for holding a government to its transparency obligations. But one conceptually significant question has received scant attention – who should be entitled to demand records under such transparency laws? The statutory answer to this question is far from uniform; indeed, on this issue transparency statutes can differ quite dramatically. Within the United States, some state governments reserve the right to request records to their own citizens. A few even bar segments of their citizenry, such as incarcerated felons, from invoking their freedom of information laws. Internationally, India limits access to its own citizens. By contrast, the Freedom of Information Act governing access to United States Government records, allows any non-foreign-state requester to obtain records. The European Union (“EU”) and Canada appear to adopt a middle ground that focuses on “physical presence” so that not only citizens, but permanent residents, can access government records.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.013 | 0.037 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.010 | 0.020 |
| Scholarly communication | 0.015 | 0.019 |
| Open science | 0.003 | 0.011 |
| Research integrity | 0.009 | 0.007 |
| Insufficient payload (model declined to judge) | 0.015 | 0.003 |
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".