Bibliographic record
Abstract
The Truth and Reconciliation Commission of Canada’s 2015 Calls to Action and the CFLA-FCAB Truth and Reconciliation Committee’s 2017 Report and Recommendations provide libraries with information for furthering reconciliation and decolonization efforts. Public libraries in Canada have responded to these documents by undertaking various initiatives, which are communicated by libraries, for example, through websites. This paper analyzed website content of five Canadian public libraries. The findings suggest that libraries have taken various initiatives in different areas including online and physical spaces, collections, and programs. Interestingly, decolonization and reconciliation related work also features in some public libraries’ strategic plans. Les appels à l'action de 2015 de la Commission de vérité et réconciliation du Canada et le rapport et les recommandations de 2017 du Comité de vérité et réconciliation de l'ACFL-FCAB fournissent aux bibliothèques de l'information pour poursuivre les efforts de réconciliation et de décolonisation. Les bibliothèques publiques du Canada ont répondu à ces documents en entreprenant diverses initiatives, qui sont communiquées par les bibliothèques, par exemple, par le biais de sites Web. Cet article analyse le contenu des sites Web de cinq bibliothèques publiques canadiennes. Les résultats suggèrent que les bibliothèques ont pris diverses initiatives dans différents domaines, y compris les espaces physiques et en ligne, les collections et les programmes. Il est intéressant de noter que les travaux liés à la décolonisation et à la réconciliation figurent également dans les plans stratégiques de certaines bibliothèques publiques.
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.012 | 0.028 |
| Science and technology studies | 0.030 | 0.008 |
| Scholarly communication | 0.017 | 0.006 |
| Open science | 0.004 | 0.014 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.011 | 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".