Bibliographic record
Abstract
The "impact bias" in affective forecasting - a tendency to overestimate the emotional consequences of a particular future event - might not be a universal phenomenon. This prediction bias occurs in part because of a cognitive process known as focalism, whereby predictors focus attention narrowly on the target event and neglect other mitigating events and circumstances. It was hypothesized that East Asians, because of their holistic tendencies, would be less susceptible to focalism and consequently to the impact bias. These hypotheses were partially supported. In Study 1, participants predicted on a cold day how happy they would be when outdoor temperatures first reached 20 degrees Celsius. When this warmer weather arrived, a comparable sample of participants reported their happiness. In Study 2, participants nominated an upcoming positive event and predicted how happy they would be two weeks later if it occurred. Two weeks later, the same participants reported their actual happiness levels. In both studies, Euro-Canadians exhibited the impact bias, predicting significantly more happiness than they experienced, but Asians did not. The Euro-Canadians predicted greater happiness than Asians, whereas actual happiness levels did not differ across cultures. In addition, a measure of cognitive process revealed that the cultural difference in prediction was mediated by the degree to which participants focused on the target event itself. These results suggest that East Asians are less prone than Westerners to the impact bias, because they focus less on the target event while generating affective forecasts. Although scores on several holism measures were not predictive of focalism or affective forecasts, the results of both studies supported the hypothesized patterns of predicted and experienced happiness as well as confirmed the expected role of focalism.
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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 | 0.001 | 0.004 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".