Libraries On the Hill:
Bibliographic record
Abstract
This exploratory study analyzes the advocacy practices and outputs of three national associations representing libraries and organizations of various types: the Canadian Association of Research Libraries (CARL), the Canadian Urban Libraries Council (CULC), and the Canadian Federation of Library Associations-Fédération canadienne des associations de bibliothèques (CFLA-FCAB). Data was collected from a variety of sources, including the associations’ websites, records of federal government consultations and lobbying activities. A thematic analysis was conducted using open coding and visual theme mapping, and the results analyzed using Schein’s model for understanding organizational culture. The results provide important insights into publicly available advocacy work by these associations since 2016. By providing the first step of quantifying advocacy work by Canadian library associations, this study lays the groundwork for further investigation to explore the impact of library association advocacy and to identify successful patterns and strategies for advocacy initiatives in the future.
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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 | 0.003 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.007 |
| Science and technology studies | 0.025 | 0.005 |
| Scholarly communication | 0.017 | 0.007 |
| Open science | 0.002 | 0.010 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.040 | 0.005 |
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".