Bibliographic record
Abstract
The paper covers the history and evolution of an annual voluntary effort covering all major public libraries in Canada, emphasising what is distinctive about it today including content and process.It describes in detail the process involved which requires only 100 hours over 2 months to collect and distribute the extensive results.The paper discusses the value of this effort to the public library community in general and to specific regions and systems in particular.A sample of the current survey instrument will be presented and reviewed briefly noting the distinctive elements of the Canadian model.The numerous reports generated annually for participants in the survey will be described and evaluated as well.The paper covers recent approaches to redefining performance measures for this national group including the place for ISO definitions.It identifies the efforts being made to develop a national benchmarking tool in key areas of national concern and a process and template for local balanced scorecard data as well.The paper concludes by identifying current statistical needs in the Canadian public library community and proposing solutions for the future, including the emphasis on a set of national key performance indicators for public libraries, sound benchmarking practices, and the evolution of a "balanced scorecard" approach to gathering and sharing data nationally in Canada.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.008 |
| Science and technology studies | 0.007 | 0.001 |
| Scholarly communication | 0.008 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.177 | 0.051 |
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".