I'm Mad as Hell, and I'm Not Going to Take It Anymore: Reports of Negative Emotions as a Self‐Presentation Tactic<sup>1</sup>
Bibliographic record
Abstract
This research investigated how self‐presentation goals can influence public expressions of negative emotions. In Study 1, participants were asked how individuals would present their negative emotions if they were trying to create each of 5 different impressions, which corresponded to 5 self‐presentation strategies identified by Jones and Pittman (1982). Results show that individuals were expected to systematically understate or exaggerate their negative emotions, depending on the impression/strategy. In Study 2, participants discussed with another person a course they were taking where they were not doing as well as they had hoped. They were instructed either to present their feelings honestly, to ingratiate, or to intimidate the other person. Compared to the honesty condition, ingratia‐tion led to fewer negative emotions being expressed, whereas intimidation led to more negative emotions being expressed. Taken together, these studies provide initial evidence about when and how self‐presentation motives can influence reports of negative emotions.
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How this classification was reachedexpand
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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 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 teacher head, 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".