Bibliographic record
Abstract
Communities of Practice (CoPs) bring together practitioners who share a common interest and provide a forum for them to improve upon their practice. The City Librarians Community of Practice was formed in late 2019 to fulfil a professional development need among librarians across the city. Librarians from across sectors were invited to join in this multi-sectoral CoP with the intent of it being an opportunity for networking, collaboration, and sharing of best practices. Multi-sectoral communities of practice are not common in the literature, with most CoPs focusing on a narrow subject area of interest or being hosted by a single institution. This study reports on the results of a survey of City librarians, including those who became members of the CoP and those who opted not to join. The survey was intended to garner anonymous feedback on the CoP, to determine its benefits, and to identify potential areas for growth and improvement. While the CoP did not directly impact practice of its members, there have been perceived indirect impacts, including the sharing of information, hearing about librarianship issues from other perspectives, and affective, social elements. Many members preferred an informal, flexible approach over a more rigid, academic slant towards meetings. Regular communication and check-ins with members and potential members is another identified way of handling the natural attrition that comes with CoPs and to continue to keep the CoP relevant and engaging for the librarians of City.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.020 | 0.042 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.012 | 0.005 |
| Scholarly communication | 0.010 | 0.006 |
| Open science | 0.002 | 0.017 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.013 | 0.002 |
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".