Developing the WECARE cross-national research alliance for investigating early childhood educators’ wellbeing
Bibliographic record
Abstract
Purpose This paper describes the development of the WECARE cross-national research alliance for investigating early childhood educators’ wellbeing, and details the experiences of some of WECARE’s 17 members. Design/methodology/approach The paper explores and situates the WECARE team’s experiences within extant literature on cross-national and collaborative research groupings alongside a strongly practical focus. Findings The study’s findings included effects of member mindsets and motivations, differentiated benefits and challenges of membership, cultural sensitivity, research capacity-building, leadership, communication, data security and planning. Originality/value Cross-national research is seen as an important part of academic researchers’ activities. Yet, little has been written about how cross-national research groups form and operate, and what benefits and challenges their members 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 | 0.154 | 0.137 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.014 | 0.006 |
| Scholarly communication | 0.012 | 0.014 |
| Open science | 0.003 | 0.035 |
| Research integrity | 0.003 | 0.007 |
| Insufficient payload (model declined to judge) | 0.008 | 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".