Bibliographic record
Abstract
The function of mood and emotion in human behavior has long been a subject of interest for researchers and lay thinkers alike. Personal experience may tell us that our moods and feelings indeed influence our judgment of things like personal happiness or aesthetic quality. The affect‐as‐information hypothesis, however, distinguishes itself from intuition in that it asserts that our mood is used as an actual source of information in these judgments. Cultures differ in values ascribed to mood and affect, and thus may influence the degree to which affect is used in judgment. The present study examines cultural differences in the use of affect, or positive and negative moods, as information in evaluative judgment. The study represents an international collaboration between Queen’s University and the University of Macau. In two experiments, we induced negative and positive moods in participants and randomly assigned them into conditions in which they were either made aware or not of the source of their mood. Participants were then asked to evaluate the attractiveness of images (Study 1) and rate their life satisfaction (Study 2). I hypothesize that the Chinese will rely less on affect as information than Canadians and propose that this attenuated dependence is mediated by lower clarity and less attention to mood on the part of the Chinese. Preliminary data from the University of Macau appear to support the hypothesis that the Chinese do not rely on affect as information.35
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".