What Is Implicit Self-Esteem, and Does it Vary Across Cultures?
Bibliographic record
Abstract
Implicit self-esteem (ISE), which is often defined as automatic self-evaluations, fuses research on unconscious processes with that on self-esteem. As ISE is viewed as immune to explicit control, it affords the testing of theoretical questions such as whether cultures vary in self-enhancement motivations. We provide a critical review and integration of the work on (a) the operationalization of ISE and (b) possible cultural variation in self-enhancement motivations. Although ISE measures do not often vary across cultures, recent meta-analyses and empirical studies question the validity of the most common way of defining ISE. We revive an alternative conceptualization that defines ISE in terms of how positively people evaluate objects that reflect upon themselves. This conceptualization suggests that ISE research should target alternative phenomena (e.g., minimal group effect, similarity-attraction effect, endowment effect) and it allows for a host of previous cross-cultural findings to bear on the question of cultural variability in ISE.
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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 | 0.005 | 0.021 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".