Bibliographic record
Abstract
Mindfulness has grown from an obscure subject to an immensely popular topic that is associated with numerous performance, health, and well-being benefits in organizations. However, this growth in popularity has generated a number of criticisms of mindfulness and a rather piecemeal approach to organizational research and practice on the subject. To advance both investigation and application, the present paper applies The Balance Framework to serve as an integrative scaffolding for considering mindfulness in organizations, helping to address some of the criticisms leveled against it. The Balance Framework specifies five forms of balance: 1) balance as tempered view, 2) balance as mid-range, 3) balance as complementarity, 4) balance as contextual sensitivity, and 5) balance among different levels of consciousness. Each form is applied to mindfulness at work with a discussion of relevant conceptual issues in addition to implications for research and practice. Plain Language Summary In order to appreciate the value of mindfulness at work researchers and practitioners might want to consider both the benefits and potential drawbacks of mindfulness. This paper presents a discussion of both the advantages and possible disadvantages of mindfulness at work organized in terms of the five dimensions of an organizing structure called The Balance Framework.
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 | 0.004 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.002 |
| Science and technology studies | 0.002 | 0.024 |
| Scholarly communication | 0.006 | 0.008 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.001 | 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".