The trade-offs schools must make: Conceptualizing an empirical study for children’s well-being in schools
Bibliographic record
Abstract
The focus of this round table presentation is to discuss the ways in which an original theoretical framework for student well-being can be operationalized in an empirical study. The well-being framework was recently developed by myself as a way to conceptualize children’s well-being in schools. Relevant philosophical, sociological and psychological perspectives on children’s well-being were identified as important when considering the role that schools have in children’s flourishing. In particular, the framework illustrates the relationships between the emergent themes of children’s well-being qua-child/qua future adult; individual and collective well-being; and well-being as harm reduction or positive affect. The empirical component for this project is a work in progress at this stage. Roughly speaking, data will be collected to gain deeper understanding of the tensions and trade-offs that are presented in the well-being in schools framework. Further, this project aims to explore the role of knowledge mobilization and assessment strategies in the area of student well-being.
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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 | 0.018 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.012 | 0.046 |
| Scholarly communication | 0.017 | 0.017 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".