How should collective and distributed skills be considered in professional skills management?
Bibliographic record
Abstract
The purpose of this paper is to discuss how collective and distributed skills are considered in a professional competence management system by associating sociological and ergonomic work approaches. Collective work in companies is currently paradoxical: on the one hand, its value is increased through various forms of cooperation, and on the other hand, it is restricted in its makeup and sustainability by new kinds of employment. However, the results of our study, carried out in a large industrial and retailing company in a high-risk sector, highlight that a “single” and individual professional competence management system is a partial system. Work contexts should take into account technical specialties, changes in populations, changes in technology, etc. An efficient skills management system therefore ought to combine both individual and collective approaches in order to anticipate organizations that promote the development of collective and/or distributed skills, and training situations that promote their construction and transmission.
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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 | 0.014 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.006 | 0.028 |
| Scholarly communication | 0.013 | 0.018 |
| Open science | 0.003 | 0.008 |
| Research integrity | 0.005 | 0.005 |
| Insufficient payload (model declined to judge) | 0.003 | 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".