On Emotionally Intelligent Time Travel: Individual Differences in Affective Forecasting Ability
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
In two studies, the authors examined whether people who are high in emotional intelligence (EI) make more accurate forecasts about their own affective responses to future events. All participants completed a performance measure of EI (the Mayer-Salovey-Caruso Emotional Intelligence Test) as well as a self-report measure of EI. Affective forecasting ability was assessed using a longitudinal design in which participants were asked to predict how they would feel and report their actual feelings following three events in three different domains: politics and academics (Study 1) and sports (Study 2). Across these events, individual differences in forecasting ability were predicted by participants' scores on the performance measure, but not the self-report measure, of EI; high-EI individuals exhibited greater affective forecasting accuracy. Emotion Management, a subcomponent of EI, emerged as the strongest predictor of forecasting ability.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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 it