Access to Information, Higher Education, and Reputational Risk: Insights from a Case Study
Bibliographic record
Abstract
Abstract Access to information and freedom of information (ATI/FOI) requests are an increasingly utilized means of generating data in the social sciences. An impressive multi-disciplinary and international literature has emerged which mobilizes ATI/FOI requests in research on policing, national security, and imprisonment. Absent from this growing literature is work which deploys ATI/FOI requests in research on higher education institutions (HEIs). In this article I examine the use of ATI/FOI requests as a methodological tool for producing data on HEIs. I highlight the data-generating opportunities that this tool offers higher education researchers and provide a first-hand account of how ATI/FOI requests can be mobilized in higher education research. I argue that despite the value of ATI/FOI requests for producing data on academic institutions, the information management practices of HEIs limit the effectiveness of ATI/FOI in ways that I detail drawing on my experience using information requests to scrutinize the quality assurance of undergraduate degree programs in Ontario. I suggest that in an age of rankings and league tables HEIs are likely to prioritize the protection of their reputation over the right of access. In conclusion I consider the implications of the article’s findings for higher education researchers and ATI/FOI users.
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How this classification was reachedexpand
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".