Japanese students’ reconceptualization of racialized English and English speakers through study abroad
Bibliographic record
Abstract
Japan’s kokusaika (internationalization), despite its literal meaning, has been considered to be a form of Westernization, largely influenced by Western countries, especially the United States (Fujimoto, 2001; Kubota, 1998, 2002). As a consequence of the Western or U.S.-favored policies, Japanese people have developed racial attitudes toward the English language and English speakers. While Japanese people have a propensity toward white people and their English varieties, they tend to show discriminatory attitudes toward those who have other racial and linguistic backgrounds (Kobayashi, 2010; Kubota & McKay, 2009). At the same time, the number of Japanese study abroad (SA) students, specifically in Kachru’s (1985) Inner Circle countries, is increasing. While some research suggests that Japanese SA students tend to develop racial attitudes and stereotype certain racial groups, there is still a dearth of studies regarding how these attitudes and stereotypes change over time. For example, scholars have not yet examined how Japanese SA students’ interactions with an unfamiliar Other in Japan influence their attitudes during their SA, and how these attitudes change again once immersed in the SA site. Thus, drawing on intergroup contact theory (Pettigrew, 1998) and critical race theory (Delgado & Stefancic, 2001), this multiple case study investigated how Japanese students (re)conceptualize their attitudes toward racialized English and English speakers through interactions with diverse English speakers. Data were collected through semi-structured qualitative interviews with six Japanese SA students. This study found that the participants’ pre-departure intergroup contact situations had been constructed according to Japan’s skewed kokusaika, which resulted in their assumption that white people were the only legitimate English speakers. However, during SA, the participants experienced frequent intergroup contacts with various English speakers, reconceptualizing their racialized views, regarding anyone as an English speaker regardless of his/her racial and linguistic backgrounds. Additionally, the participants acknowledged that they would like to keep their transformed views after SA. This thesis concludes with implications for pre- and post-SA English language teaching in Japan so that the potential for SA to de-racialize Japanese students’ attitudes toward English and English speakers might be realized.
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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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.006 | 0.005 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.001 | 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".