Ethnocentrism, Cultural Traits, Beliefs, and English Proficiency: A Japanese Sample
Bibliographic record
Abstract
This article reports on a study investigating the role in second language (L2) learning of ethnocentrism, cultural and personality traits, and acceptance of values and beliefs expressed in myths and proverbs. Although scholars have conjectured that these factors may play an important role in L2 proficiency, there has been very little empirical investigation of this issue. In our study, 108 adult Japanese living in North America were asked how much they agreed with or accepted statements expressing ethnocentric views about Japanese culture and language that described their shyness, inwardness, and groupist tendencies and that indicated certain Japanese values and beliefs as expressed through myths and proverbs. Three sets of variables were tested: (a) participants’ ethnocentrism, (b) willingness to acknowledge certain cultural traits as being characteristic of Japanese as a group, and (c) willingness to accept the validity of Japanese‐oriented myths and proverbs. The participants’ responses on these variables were correlated with their levels of English proficiency as measured by a cloze test, a self‐rated ability sacle, and a self‐rated performance scale. It was hypothesized that the higher the participants’ responses on these variables, the lower their proficiency levels in English would be. Results suggest that these cultural traits and beliefs play a role in L2 learning but provide little evidence for an effect of ethnocentrism as the term is commonly defined.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".