Robotisering en de gevolgen voor arbeidsbelasting en het arbeidsdeskundig vak
Bibliographic record
Abstract
Met dit onderzoekscahier kunnen arbeidsdeskundigen de kansen van robotisering voor specifieke groepen met beperkingen beter benoemen. Het gaat er natuurlijk ook om deze kansen te benutten. De arbeidsdeskundige kan hierbij een cruciale rol vervullen, zowel in het kader van preventie als in het kader van re-integratie. De arbeidsdeskundige zal hiervoor de kennis en vaardigheden moeten verwerven om: ■ de aard van de robotondersteuning te kunnen herkennen; ■ de mate van de robotondersteuning te kunnen herkennen; ■ de verschuiving in arbeidsbelasting in kaart te kunnen brengen; ■ bedreigingen en kansen voor mensen met beperkingen te kunnen benoemen; ■ bedreigingen weg te nemen of te reduceren en kansen te benutten.
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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 | 0.005 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.008 | 0.004 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.161 | 0.089 |
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".