Bibliographic record
Abstract
SUMMARY Librarians are well positioned to embrace the journey towards a learning culture; we have resources and we have incentive! Teetering on the edge of information technology, libraries are committed to continuous change for the benefit of our customers. To fulfill this promise, staff must keep pace with new technologies, products, and an increasing demand for new services in an environment with shrinking human resources. There is more to learn and less time in which to learn it. This paper describes a proactive, team-based approach used to create a learning culture in one library. Staff act as peer learners and teachers to educate themselves and each other about all aspects of their reference work such as approaches to service, orientation for new members, learning and evaluating new tools, and discussing the development of new services. The whole is greater than the sum–this dynamic, shared learning environment embraces diverse learning styles including discovery, discussion, demonstration, presentation, homework, questioning, and hands-on practice. Analysis of feedback from students and challenging questions at the reference desk grounded the experience and made it immediately relevant and useful. This strategy furthers the goal of the learning organization where members share the responsibility of learning. The outcomes are an enriched collective knowledge and understanding, a sustainable model for continuous learning, social connectivity, and team experience.
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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 | 0.019 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.017 | 0.039 |
| Scholarly communication | 0.026 | 0.016 |
| Open science | 0.002 | 0.027 |
| Research integrity | 0.004 | 0.007 |
| Insufficient payload (model declined to judge) | 0.008 | 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".