University of Calgary properly withheld some information but improperly withheld other information
Bibliographic record
Abstract
An Adjudicator with the Office of the Information and Privacy Commissioner has determined that the University of Calgary properly withheld some information that had been requested by an individual, but that it improperly withheld other information and failed to meet its duty to assist. The Applicant, a former employee of the U of C, requested information held by other employees, a Wellness Centre and a doctor associated with the Wellness Centre. The Public Body provided some information to the Applicant, but withheld other information, citing several sections of the Freedom of Information and Protection of Privacy Act (FOIP). The Public Body did not provide information from the Wellness Centre or the doctor, instead advising the Applicant to request those records directly from the Wellness Centre. Following an inquiry into the matter, Adjudicator Wade Riordan Raaflaub determined the Public Body properly withheld some information, but he ruled the Public Body did not properly apply some sections of FOIP and ordered the Public Body to release that information. Riordan Raaflaub found the Public Body failed to meet its duty to assist the Applicant, as it failed to make every reasonable effort to search for the requested records and/or inform the Applicant about the search. He found the Public Body had no duty to search for and provide records from the Wellness Centre, with one exception.
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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 | 0.016 | 0.047 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.021 | 0.010 |
| Scholarly communication | 0.021 | 0.004 |
| Open science | 0.004 | 0.006 |
| Research integrity | 0.024 | 0.019 |
| Insufficient payload (model declined to judge) | 0.024 | 0.008 |
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".