Freedom of Information Requests and Peer Review Reports
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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 […]
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.007 | 0.022 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.003 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it