The library as a learning organization and the climate for updating in a period of rapidly changing technologies
Bibliographic record
Abstract
Abstract Rapidly changing technologies in libraries require continuous learning on the part of staff, particularly librarians, to keep up to date. This paper examines some of the factors affecting the participation of librarians in professional development activities. Reference librarians working in large urban public libraries in Ontario were surveyed in 2001. Data on their participation in formal and informal learning activities, together with information about their perceptions of their libraries' environment with respect to updating and learning were obtained from 553 respondents. The analysis shows that an environment which encourages learning plays a role in librarians' participation in professional development, particularly participation in informal learning activities, such as discussions with colleagues, reading and conference participation. Surprisingly, the climate for updating was found to reduce participation levels in informal activities. Having a supportive manager who provides feedback about job performance, assigns opportunities to develop and strengthen new skills and supports attempts to acquire additional training plays a positive role in the participation of reference librarians in informal professional development activities. If libraries are truly determined to become learning organizations, they must first examine their own culture of learning and climate for updating.
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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 | 0.004 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.006 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".