Optimism and realism: A review of self‐efficacy from a cross‐cultural perspective
Bibliographic record
Abstract
T his review critically examines much of the research investigating self‐efficacy beliefs through cross‐cultural comparisons. Two sets of cross‐cultural comparison groups are examined: Asian (or immigrant Asian) versus Western, and Eastern European versus Western European and American groups. After an introduction to self‐efficacy theory, some cross‐cultural aspects of self and self‐beliefs are discussed, and the cultural dimensions of individualism and collectivism are introduced. Analysis of the articles focuses on differences in levels of efficacy beliefs, calibration of beliefs with performance, methodological problems, and implications for practice. Almost all of the 20 studies reviewed found efficacy beliefs to be lower for non‐Western cultural groups, but in some cases these lower beliefs were more predictive of subsequent functioning. There is some evidence that the mean efficacy beliefs of a cultural group are modified through immigration or political changes. For some non‐Western groups, collective efficacy appears to operate in much the same way as self‐efficacy operates for Western groups. Realistic—as opposed to optimistic—efficacy beliefs do not necessarily predict poor performance for all cultural groups, as has been suggested by self‐efficacy theory. Only a minority of the studies included measurement of cultural dimensions such as individualism and collectivism, although most of the studies based conclusions on assumed cultural differences. In some cases, self‐efficacy was poorly defined and bore little resemblance to theoretically derived definitions. Conclusions from this research have implications especially for applied settings in education and business: Efficacy beliefs and performance appear to be enhanced when training approaches are congruent with the individual's sense of self. Lower levels of self‐efficacy beliefs found in some collectivist groups do not always signify lower subsequent performance, but are instead reflective of differing construals of self.
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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 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.008 | 0.007 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| 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".