Robotisering en de gevolgen voor arbeidsbelasting en het arbeidsdeskundig vak
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.008 | 0.005 |
| Meta-epidemiology (narrow) | 0.004 | 0.005 |
| Meta-epidemiology (broad) | 0.005 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.006 | 0.003 |
| Research integrity | 0.005 | 0.007 |
| Insufficient payload (model declined to judge) | 0.000 | 0.013 |
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 it