Why Do People Need Self-Esteem? A Theoretical and Empirical Review.
Bibliographic record
Abstract
Terror management theory (TMT; J. Greenberg, T. Pyszczynski, & S. Solomon, 1986) posits that people are motivated to pursue positive self-evaluations because self-esteem provides a buffer against the omnipresent potential for anxiety engendered by the uniquely human awareness of mortality. Empirical evidence relevant to the theory is reviewed showing that high levels of self-esteem reduce anxiety and anxiety-related defensive behavior, reminders of one's mortality increase self-esteem striving and defense of self-esteem against threats in a variety of domains, high levels of self-esteem eliminate the effect of reminders of mortality on both self-esteem striving and the accessibility of death-related thoughts, and convincing people of the existence of an afterlife eliminates the effect of mortality salience on self-esteem striving. TMT is compared with other explanations for why people need self-esteem, and a critique of the most prominent of these, sociometer theory, is provided.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".