The challenges of a 'virtual, in-residence Winter-School' on designing interventions Work, Organization, and Personnel Psychology in multicultural and glo-cal contexts.
Bibliographic record
Abstract
The article offers a description of the Winter School (WS), organized as a part of the Erasmus Mundus Joint Master's degree in Work, Organization and Personnel Psychology (WOP-P). This is an intensive learning unit that lasts five months, with a peak in-residence period of two weeks, in which Master's students from the different universities of the consortium, and also students from the six partners universities (USA, Canada, and Brazil), come together to work and live in an enriching environment that many students describe as unique. The XIV edition of this program experienced a major disruptive change due to the lockdown dictated by governmental restrictions related to the COVID-19 crisis. This lockdown restricted mobility, and the program was converted to a virtual environment. Here we present the difficulties and changes that had to be addressed, and the lessons learned from this challenging digital experience
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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 | 0.024 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.012 | 0.013 |
| Scholarly communication | 0.012 | 0.006 |
| Open science | 0.004 | 0.015 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.009 | 0.002 |
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".