Creating a Forum for Library Professionals: A Case Study of CALA Canada Chapter
Bibliographic record
Abstract
This paper examines the establishment, growth, achievements, and future planning of the CALA Canada Chapter. Since its inception in June 2018, the Chapter has experienced significant growth, with the number of members doubling, and the number of life members also doubling. Currently there are a total of thirty members in the Chapter, comprising ten life members, eight overseas members, and seven student members, with the majority residing or working in Ontario. The Chapter has achieved notable milestones, including the organization of successful events such as conferences, workshops, and networking sessions. The Chapter has also contributed to the development of the library profession in Canada, particularly by promoting diversity and inclusivity. Looking forward, the Chapter plans to expand its reach and increase its membership by promoting itself in other regions of the country. The Chapter aims to continue providing valuable resources, programs, and opportunities for its members to enhance their professional development and foster collaboration. Through these efforts, the Canada Chapter aims to play an essential role in advancing the library profession in Canada and promoting its growth and innovation. PDF
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How this classification was reachedexpand
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | Scholarly communication Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: yes | Qualitative | low |
| gpt | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: yes | Qualitative | medium |
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 | 0.005 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.066 | 0.012 |
| Scholarly communication | 0.012 | 0.004 |
| Open science | 0.004 | 0.012 |
| Research integrity | 0.005 | 0.006 |
| 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, unvalidatedLabeled directly by 2 models reading the full record.
The models disagree on parts of this classification; every voice is preserved in the section at the end of the page.
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".