Beyond All Splits: Envisioning the Next Generation of Science on Mindfulness and Compassion in Schools for Students
Bibliographic record
Abstract
Abstract Objectives This paper describes the emergence of the scientific study of mindfulness in schools; summarizes findings of experimental research on the impacts of school-based mindfulness programs (SBMPs) on student outcomes in prekindergarten, primary, and secondary school settings (ages 4–18 years); discusses scientific limitations and wider critiques of this work; and offers suggestions for future research. Methods Public data are used to describe the emergence of science on SBMPs, the foci of this research, and the academic disciplines contributing to it. A narrative summary of scientific findings regarding the impacts of SBMPs on students, and critiques of this work, is also presented. Results Research is increasing and is primarily psychological and prevention-oriented. Evidence shows SBMPs can enhance students’ self-regulation abilities, but SBMPs’ impacts on other student outcomes at different ages are equivocal. The current research has significant limitations, and these, alongside wider critiques of the work, suggest important directions for research. Conclusions In the next generation of science, we suggest (a) improving the experimental research; (b) expanding developmental research; and (c) re-envisioning assumptions, theories, and methods in research to go “beyond all splits” towards a non-dualistic and relationally, culturally, contextually, ethically, and developmentally grounded science on mindfulness and compassion for students in schools.
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 | 0.069 | 0.058 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.006 | 0.036 |
| Scholarly communication | 0.012 | 0.025 |
| Open science | 0.003 | 0.012 |
| Research integrity | 0.007 | 0.017 |
| Insufficient payload (model declined to judge) | 0.004 | 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".