Flexible and Static Containers for Storing and Sharing Knowledge and Wisdom
Bibliographic record
Abstract
This paper reports on Kenyan university women’s relation of two concepts: knowledge and wisdom. Their uses of these terms suggest a need to reconsider traditional knowledge (TK) in librarians’ practice in information literacy programs as a means to reduce the disjuncture between the dominant educational practices and wisdom, or TK.Cette communication porte sur la relation des femmes universitaires kényanes avec deux concepts : la connaissance et la sagesse. Leur utilisation de ces termes suggère un besoin de reconsidérer la connaissance traditionnelle dans les pratiques des bibliothécaires relativement aux programmes de maîtrise de l’information afin de réduire la disjonction entre les pratiques pédagogiques dominantes et la sagesse, ou la connaissance traditionnelle.
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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 | 0.006 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.010 |
| Science and technology studies | 0.009 | 0.012 |
| Scholarly communication | 0.018 | 0.040 |
| Open science | 0.004 | 0.016 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.014 | 0.004 |
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".