Bringing coherence to positive psychology: Faith in humanity
Bibliographic record
Abstract
Currently, positive psychology is experiencing problems with coherence, and the field could benefit from more organizing concepts linking disparate findings and researchers within the field. This incoherence can be seen in several domains. At a conceptual level, the field has produced an abundance of important studies clarifying predictors of well-being, but no consistent theory has emerged explaining why these factors predict well-being. In addition, disunity has emerged between first wave positive psychologists and second wave positive psychologists, and also between practitioners and researchers. The field could benefit from more unifying constructs that explain links between constructs and practices within positive psychology. Faith in humanity (FIH) has potential as a unifying construct. FIH is like a forgotten sibling whose important story is mentioned rarely and mainly obliquely. In fact, this construct, though seldom mentioned, already implicitly pervades much of positive psychology, and the field would benefit by explicitly recognizing this fact.
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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 | 0.007 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.052 |
| Scholarly communication | 0.010 | 0.009 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.002 | 0.009 |
| 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".