Exploring lived experiences of Black female English teachers in South Korea: understanding travelling intersectionality and subjectivities
Bibliographic record
Abstract
English language teaching has become a global phenomenon, involving racially and culturally diverse teachers travelling across national borders. While these transnational teachers bring diversity to host countries, the superiority of White native English speakers continues to be reinforced. This raciolinguistic ideology can uniquely shape the subjectivities of English language teachers of colour who sojourn abroad. Focusing on three African American female teachers of English who were participating in the English Program in Korea (EPIK) to teach in South Korean schools and posting YouTube videos to describe their experiences, this qualitative study examined the nature of their experiences and intersecting subjectivities regarding race, colour, language, gender, and nationality, as well as privilege and marginality as they are expressed online. The analysis focused on how intersectionality, a notion originally developed to describe Black women’s unique experiences in the United States, would be applied to this transnational context. A thematic analysis of a total of 12 videos revealed these EPIK teachers’ multifaceted and negotiated subjectivities as American teachers, victims of racial prejudice, and ambassadors with a mission to educate local people. These subjectivities signify the intersectionality of privilege and marginality, which are embedded in the local and global ideologies and power relations.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.009 | 0.006 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".