From Retreat Center to Clinic to Boardroom? Perils and Promises of the Modern Mindfulness Movement
Bibliographic record
Abstract
From its venerable Buddhist roots, mindfulness training (MT) has spread rapidly across the globe in the past few decades due to its strong salutary claim, i.e., the notion that meditation practice is an efficacious means for self-improvement. However, concerns have arisen that the appropriation of MT techniques from classical Buddhist tradition into modern secular practice has diluted the benefits of these practices. The “great danger” to the movement is that inadequately adapted MT techniques, combined with unreasonable inflation of expectations regarding MT’s benefits, may undermine MT’s true potential to effect positive change in the world. And yet, these concerns can be mitigated by consideration of the salutary claim as a persistent “quality check” on MT efficacy. It is argued that scientific investigation can take an important role in delineating the necessary characteristics for fulfilling mindfulness’ salutary claim, as well as identifying contraindicated techniques and risk factors for training. By accepting that we cannot control the spread of MT into commercial domains, researchers may still work to distinguish “right” from “wrong” mindfulness through empirical study. In this way, modern science may help to realize the salutary claim and even contribute to classical Buddhist conceptions of mindfulness, advancing our understanding of how best to promote 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 | 0.012 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.010 |
| Scholarly communication | 0.005 | 0.011 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.005 | 0.011 |
| Insufficient payload (model declined to judge) | 0.016 | 0.003 |
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".