On a heuristic evaluation system for Industry 5.0 with respect to interventions: the case of training in businesses
Bibliographic record
Abstract
Manufacturing has been undergoing many changes, with the latest one being the paradigm shift to Industry 5.0. In this long procedure, training is required at any level, from operators to managers. Thus, interventions must be made so that Teaching and Learning Factories are upgraded towards integrating Industry 5.0. To this end, an evaluation system has to be made, assessing the feasibility of the three pillars’ integration. This procedure can concern a qualitative assessment (or a quantitative one) of the feasibility and the other implicated concepts, such as upskilling. At the same time, multilevel metrics are relevant, such as Key Performance Indicators (KPIs) related to company practices, manufacturing itself, jobs and trainees. Herein, a summative differential evaluation scheme, based on heuristic aspects, is explored, under the framework of the aforementioned TLF interventions. Examples of companies’ ex-ante characterization are given. Then, potential extensions are being discussed towards achieving formative evaluation and potentially towards KPIs.
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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 | 0.087 | 0.103 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.003 |
| Science and technology studies | 0.004 | 0.009 |
| Scholarly communication | 0.009 | 0.004 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.010 | 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".