Personal Information and the Public Library: Compliance with Fair Information Practice Principles / Les renseignements personnels dans les bibliothèques publiques : le respect des principes d'équité dans les pratiques de collecte de renseignements
Bibliographic record
Abstract
Libraries collect personal information from users and link that information to internal library records.Although they fiercely protect the privacy of their patrons, libraries cannot ensure that personal information will remain confidential.Patrons must therefore have sufficient information to make informed decisions about release of personal data.Privacy notices are the accepted mechanism for providing this information.Our study demonstrates, however, that Ontario public libraries rarely provide notice to patrons regarding information collection and use.Smaller libraries and those without MLS-trained staff are less likely to provide notice, suggesting that resources and/or staff training may contribute to this lack.We suggest that national or provincial organizations may want to support libraries in the development of privacy policies.
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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 | 0.133 | 0.208 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.016 | 0.041 |
| Scholarly communication | 0.023 | 0.016 |
| Open science | 0.003 | 0.012 |
| Research integrity | 0.008 | 0.007 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".