Learning histories: spanning the great divide
Bibliographic record
Abstract
Purpose The purpose of this paper is to suggest the use of a new action research methodology, the learning history, to study knowledge transfer initiatives. Design/methodology/approach An overview of the literature on learning histories is followed by the results of a case study, where a learning history is used to transfer humanistic practices from an American health care model to a Quebec setting. Findings This study demonstrates how the learning history method can act as a catalyst to accelerate the knowledge transfer process. It has helped researchers and practitioners recognize and address the challenges involved in implementing change and transferring new knowledge in an organization. Research limitations/implications Although the learning history provides a fresh and effective way to study learning and knowledge concepts, the potential of this new methodology in studying knowledge transfer activities has not been fully explored. The limitations are primarily those associated with the amount of work involved in a developing a learning history as well as the courage and honesty it requires. Practical implications Approaches to improving learning from experience and descriptions about how to capture and disseminate knowledge within organizations are somewhat limited. The findings of this study offer practitioners and researchers guidance on how to accelerate the implementation of future initiatives knowledge transfer. Originality/value By linking learning histories to knowledge transfer, this article provides a fresh new approach to studying how knowledge can be transferred from researchers to practitioners and bridging what some have called “the great divide” between these two communities.
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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 | 0.023 | 0.045 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.011 | 0.064 |
| Scholarly communication | 0.014 | 0.033 |
| Open science | 0.003 | 0.015 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.007 | 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".