The Intersection of Freedom of Information, Privacy Legislation and Library Services in Canadian Jurisdictions
Bibliographic record
Abstract
Abstract The intersection of freedom of information, privacy legislation and library services may be interpreted as the relation between two bodies (law and library) and how they influence one another directly and indirectly. This means library services can be shaped enormously by both federal and provincial freedom of information and privacy laws. We notice that there are cases in various Canadian courts involving disagreements concerning the rule of law in the fields of freedom of information and privacy with libraries. The combined effects of legislation and stronger library policies may make it more challenging for users to understand how to use shared library resources and services properly. For many libraries, this means operational policies and professional ethics codes have to be revised to strictly respect the users and employees’ confidentiality rights. The research method used for this paper included a search of relevant Canadian court cases as case studies.
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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 | 0.011 | 0.030 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.012 | 0.025 |
| Science and technology studies | 0.040 | 0.029 |
| Scholarly communication | 0.017 | 0.005 |
| Open science | 0.003 | 0.009 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.010 | 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".