Bibliographic record
Abstract
In the present-day world of work, characterized by the globalization of markets and rapid technological change, company directors are turning toward new types of work organization and product diversification in order to contend with increasingly fierce competition. In such a context, workplace training has become a critical issue for companies. Though there are different types of workplace training, the instructors are usually experienced employees who have been asked to train new employees. Incorporating training into the company’s production activities is complex because it creates a situation where the training activity comes into contact with all the other activities. The present article reports on ergonomic research intervention conducted in the meat processing sector which sheds light on the difficulties and challenges that structured training in an SME production system poses for instructors. The data collected here showed that worker-instructors were poorly prepared to train new workers and that the role of an instructor was not sufficiently appreciated in the company. Furthermore, our study pointed out that, to organize learning situations, instructors had to consider several conditions (organizational, technical, physical, and social) and make compromises between what they would have liked to do and what the conditions allowed them to do. Avenues for improvement are suggested to help create greater recognition of the instructors’ role and provide them with more support in the implementation of training activities. The observations made in this study can serve as food for thought for anyone interested in workplace training conditions.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.000 |
| 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".