Are Implicit Self‐Esteem Measures Valid for Assessing Individual and Cultural Differences?
Bibliographic record
Abstract
OBJECTIVE: Our research utilized two popular theoretical conceptualizations of implicit self-esteem: 1) implicit self-esteem as a global automatic reaction to the self; and 2) implicit self-esteem as a context/domain specific construct. Under this framework, we present an extensive search for implicit self-esteem measure validity among different cultural groups (Study 1) and under several experimental manipulations (Study 2). METHOD: In Study 1, Euro-Canadians (N = 107), Asian-Canadians (N = 187), and Japanese (N = 112) completed a battery of implicit self-esteem, explicit self-esteem, and criterion measures. Included implicit self-esteem measures were either popular or provided methodological improvements upon older methods. Criterion measures were sampled from previous research on implicit self-esteem and included self-report and independent ratings. In Study 2, Americans (N = 582) completed a shorter battery of these same types of measures under either a control condition, an explicit prime meant to activate the self-concept in a particular context, or prime meant to activate self-competence related implicit attitudes. RESULTS: Across both studies, explicit self-esteem measures far outperformed implicit self-esteem measures in all cultural groups and under all experimental manipulations. CONCLUSION: Implicit self-esteem measures are not valid for individual or cross-cultural comparisons. We speculate that individuals may not form implicit associations with the self as an attitudinal object.
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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 | 0.008 | 0.040 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".