What's wrong with this picture? The case of access to information requests in two continental federal states – Germany and Switzerland
Bibliographic record
Abstract
More than 80 access to information (ATI) laws exist worldwide. Their primary objectives are to increase transparency and accountability in government. Given the similarity in the components of ATI laws across countries, one could expect per capita usage of the laws to be roughly similar. However, comparing the number of requests in seven countries, we found that far fewer requests are being made in Switzerland and Germany than in Canada, Ireland, Mexico, India, and the UK and that, in contrast to these five, the number is not increasing. Drawing on 28 semi-structured interviews with experts on the Swiss Law on Transparency (LTrans) and German FOI Law (IFG), we offer three primary explanations for the low use of the laws. The first is that few people are aware of the law in either country as a consequence of little promotion of the laws. The second is that people might have more interest in information held at the state or local level than at the federal level. The third is that other avenues to information in Switzerland reduce interest in using the LTrans and a culture of “ amtsgeheimnis”, or official secrecy, in Germany inhibits the administration from willingly disclosing information. We examine these hypotheses against the situation in the UK, where awareness of the FOI law is known to be high and the number of requests is high and has been on the rise for the past four years.
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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 | 0.005 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.013 | 0.010 |
| Scholarly communication | 0.008 | 0.005 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.006 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 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".