Whose Knowledge? Whose Management? Cognitive Considerations for the Provision of Virtual Library Services to School Communities
Bibliographic record
Abstract
Participating in knowledge management requires school libraries and libraries that serve school communities to understand the cognitive aspects of knowledge acquisition and sharing, and the inadequacies of knowledge management initiatives driven by information technology. Discontinuities in the data-information-knowledge continuum make information fundamentally different from knowledge. Three knowledge principles are critical to successful design and delivery of virtual services, and a knowledge management approach may require reduction in the amount and speed of information the library delivers in order to convert knowledge to value for the school. To manage knowledge effectively, we need to shift our focus to the social context and intentionally and carefully manage the tacit knowing of librarians.
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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 | 0.029 | 0.053 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.024 | 0.056 |
| Scholarly communication | 0.041 | 0.025 |
| Open science | 0.004 | 0.010 |
| Research integrity | 0.008 | 0.007 |
| Insufficient payload (model declined to judge) | 0.005 | 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".