Social Media Use By Ontario University Libraries: Challenges and Ethical Considerations
Bibliographic record
Abstract
The application of social media by academic libraries is re-shaping traditional ideas of library services. The use of social media in Ontario’s university libraries demonstrates the divergent modes by which information technologies are utilized, as well as the challenges facing libraries in both adopting and using these tools.L’application des médias sociaux par les bibliothèques universitaires transforme les idées traditionnelles des services en bibliothèque. L’utilisation des médias sociaux dans les bibliothèques universitaires de l’Ontario démontre des modes divergents d’utilisation des technologies de l’information, ainsi que les défis auxquels font faces les bibliothécaires en termes d’adoption et d’utilisation de ces outils.
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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 | 0.087 | 0.120 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.030 | 0.031 |
| Scholarly communication | 0.024 | 0.008 |
| Open science | 0.004 | 0.008 |
| Research integrity | 0.008 | 0.007 |
| Insufficient payload (model declined to judge) | 0.004 | 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".