Positive Psychology: Introduction to the Special Issue (In memoriam: Chris Peterson) Psicología Positiva: Introducción al número especial (In memorian, Chris Peterson)
Bibliographic record
Abstract
This special issue of Terapia Psicologica includes relevant contributions to the field of Positive Psychology, written by international researchers on well-being from Chile, Canada, Italy, Mexico, Netherlands, Spain, and USA,. Rather than viewing the field complacently, this issue contains critical reviews of the literature, which raises intriguing questions about the interpretation of past research and the possibility of answering those questions in the future. Original research is also presented and the methods used are both correlational and experimental, with cross-sectional and longitudinal designs. The content of these works include developmental and health-related issues, the positive and negative impact of traumatic experiences, and the effect of controlled experimental interventions on improving well-being. In conclusion, this issue reflects the increasing maturity of the Positive Psychology field and the growing complexity of the issues addressed as well as the methodologies used. Resumen
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.005 | 0.010 |
| Insufficient payload (model declined to judge) | 0.047 | 0.029 |
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".