Developing ethical guidelines for positive psychology practice: An on-going, iterative, collaborative endeavour
Bibliographic record
Abstract
As positive psychology has developed as a field, questions have arisen around how to ensure best practice, including with respect to ethics. This issue is particularly pertinent vis-à-vis its applied dimensions, such as positive psychology interventions by students and graduates of MAPP programmes. However, the field has hitherto lacked clear ethical guidelines to assist practitioners. Aiming to address this gap, the authors have devised a set of guidelines, in collaboration with key stakeholders across the positive psychology community, published in the International Journal of Wellbeing. The current article briefly summarises the importance, development, content, and future directions of these guidelines, thus providing a concise overview of this important project. It is hoped that this article, together with the guidelines themselves, will not only highlight the importance of ethical practice, but offer practical suggestions for guiding practitioners in the field.
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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 | 0.341 | 0.388 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.007 | 0.004 |
| Science and technology studies | 0.013 | 0.019 |
| Scholarly communication | 0.022 | 0.019 |
| Open science | 0.007 | 0.024 |
| Research integrity | 0.011 | 0.028 |
| Insufficient payload (model declined to judge) | 0.007 | 0.004 |
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".