Getting ready for the bad times: self‐esteem and anticipatory coping
Bibliographic record
Abstract
Abstract When we cannot alter the characteristics of an aversive event, we are still able to prepare ourselves for what is to come. In other words, we can engage in ‘anticipatory coping.’ Known self‐esteem differences in self‐regulation led to the prediction that low self‐esteem (LSE) individuals would evidence different anticipatory coping patterns than high self‐esteem (HSE) people. HSE and LSE participants were faced with either a low or high probability of engaging in a painful task. They were told about, and given the opportunity to engage in, a preparatory strategy aimed at minimizing discomfort during the painful task. Those participants in the low probability condition prepared for the painful task less than did those participants in the high probability condition. As hypothesized, the effect of probability condition was more pronounced for HSE, compared to LSE, participants. Also, in the low probability condition, there was a trend towards LSE participants preparing more than HSE participants. Copyright © 2004 John Wiley & Sons, Ltd.
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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 | 0.005 |
| 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 | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".