Potential Models of Group Learning in Production
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
Working in groups is beneficial for many complex production jobs as groups can have the cognitive and physical capacity that lacks from individuals. The group learning process is complicated when, in addition to individual learning by doing, the number of workers and knowledge transfer have their effects. Production managers need tools for analyzing and predicting group performance and learning over future production periods. Mathematical learning curve models are one of those tools that managers use, with a few are available for groups. This paper reviews potential group learning curve models for production environments. The models are fitted to data from an assembly experiment consisting of different group sizes and repetitions. The results show that more parameters improve the fit. A qualitative evaluation has been performed to answer how well the models reflect group learning and support decision making in production and how their prediction of data could be improved. The results suggest that the S-shaped model performed the best making it a potential one for describing learning in groups in production environments. The paper also suggests future directions along with this line of research.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it