Freedom of Information Requests and Peer Review Reports
Bibliographic record
Abstract
In this commentary, we briefly assess the legal context of Freedom of Information (FOI) requests, such as the Canadian Freedom of Information and Protection of Privacy Act (FIPPA), within peer review. FOI/FIPPA requests to government institutions need to be carefully vetted, while the privacy of both the applicant and subject should be protected. FOIs related to misconduct are valid, but those that are based merely on inquisitiveness or that seek access to confidential emails and information, are contentious. We believe that access to “research material”, including emails, should be limited to misconduct investigations. In some countries, it is possible to request information from a government institution such as a public university, via FOI requests, about records at such institutions. FOI requests are associated with issues of accountability and transparency of government operations,1 but they may also encompass clauses regarding the protection of privacy, both of the applicant and of the subject of the FOI request, such as the FIPPA in each Canadian province, for example, in British Columbia (BC).2 FOI requests cover records that are only under the public body’s control and custody, such as the operation and administration of a governing body. Researchers who work at a public university conduct their own research, teach students, and spend time for service. These 3 functions result in records that belong to the researchers and are not the property of the public university, and so should be excluded from FOI requests to protect the researchers’ academic freedom and intellectual property. In academic […]
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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 | 0.399 | 0.765 |
| Meta-epidemiology (narrow) | 0.002 | 0.003 |
| Meta-epidemiology (broad) | 0.005 | 0.004 |
| Bibliometrics | 0.010 | 0.011 |
| Science and technology studies | 0.018 | 0.033 |
| Scholarly communication | 0.053 | 0.027 |
| Open science | 0.016 | 0.019 |
| Research integrity | 0.057 | 0.038 |
| Insufficient payload (model declined to judge) | 0.036 | 0.034 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".