Methodological Issues in the Assessment of the Affective Component of Subjective Well-Being
Bibliographic record
Abstract
Abstract One important area of positive psychology examines happiness and subjective well-being (SWB; Diener, 2000). Diener (1984) noted that SWB has an affective and a cognitive component. The cognitive component of SWB is assessed with life satisfaction judgments (Diener, Emmons, Larsen, & Griffin, 1985). The affective component assesses the amount of pleasant and unpleasant experiences in people’s lives (e.g., Schimmack, Diener, & Oishi, 2002). Early on, SWB researchers noted the limitations of research programs that focused exclusively on negative states such as depression and anxiety. In an influential article, Diener (1984) proposed that SWB is more than the absence of negative affect (NA). Although low levels of NA are important for SWB, high levels of positive affect (PA) are also important. Diener’s (1984) conception of SWB in terms of high PA and low NA implies that PA and NA are separable components of SWB. In other words, measures of PA show discriminant validity from measures of NA. If PA and NA were bipolar opposites, then minimization of NA would also maximize PA. In contrast, the distinction of PA and NA as separate components of SWB implies that PA and NA have different causes and consequences.
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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 | 0.186 | 0.255 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.006 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.006 | 0.004 |
| Research integrity | 0.001 | 0.005 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".