Learning the Secrets of the Craft Through the Real-Time Experience of Experts : Capturing and Transferring Experts’ Tacit Knowledge to Novices
Bibliographic record
Abstract
Mass retirement in the baby-boomer generation has led to the current challenge of renewing mentoring in skilled trades, which have traditionally made it possible to transmit the know-how required in manual expertise. A study conducted with France’s largest electricity supplier investigated how the combination of Activity Theory, video ethnography, and psychological verbalisation methods can help to enhance both the preservation of knowledge capital and occupational training. The study led to the development of a novel method for capturing and transferring tacit and explicit knowledge embodied in professional gestures of experts through the building of a new kind of educational media for occupational training. This device, called MAP, allowed operators to learn directly through the real-time experience of experts. A qualitative evaluation of the device showed an improvement in training. These results convinced the company to adopt the device for institutional use. This demonstrates the proposed method’s usefulness for tacit knowledge capture and transfer.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".