Finding consensus on well-being in education
Bibliographic record
Abstract
Research on well-being and concern over the well-being of students and teachers has grown dramatically in recent years. Researchers and reformers in positive psychology and education, self-determination theory, social and emotional learning, liberal-democratic political and educational philosophy, and neo-Aristotelian theories of flourishing and character education have played formative and intersecting roles in what is now an international movement to promote the lifelong flourishing of students as an alternative to a human capital and economic growth focus for education. This article defends this flourishing-focused reorientation of education policy and practice, using a value-led and evidence-informed methodology. It sorts through the conceptual disputes and clarifies the ethical considerations that should guide efforts to advance the well-being of students and teachers, assesses key claims and arguments, and brings together compatible aspects of the leading philosophical and psychological perspectives on flourishing as an aim of education. It identifies ethically and evidentially justifiable points of consensus on well-being and flourishing in education, presents a consensus model of relationships between educational environments, learning, and flourishing, and concludes with some recommendations for educational policy and practice.
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.
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 | 0.145 | 0.249 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.011 | 0.010 |
| Science and technology studies | 0.005 | 0.027 |
| Scholarly communication | 0.020 | 0.026 |
| Open science | 0.006 | 0.020 |
| Research integrity | 0.010 | 0.016 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".