Identity Construction and Negotiation of Classroom CoP Members in Global Englishes Course: A Higher Education Context in Thailand
Bibliographic record
Abstract
This ethnographic study examines identity negotiation and construction inside a Global Englishes (GE) classroom community of practice in a Thai higher education context. Drawing on the communities of practice (CoP) framework (Lave & Wenger, 1991; Wenger, 1998), the study theorizes the academic GE classroom as a CoP and explores how participants construct identities while engaged in the joint enterprise of becoming English as a lingua franca (ELF) users and engaging in classroom activities using a shared repertoire of humour and shared narratives. The findings revealed the emergence of multiple identities from the overlapping characteristics of the academic classroom, and raised questions regarding legitimate peripheral participation (LPP) and identity trajectories as they intersect the egalitarian notion of ELF, including semi-expert identity, reverse identity, and bullying. Furthermore, the study highlighted the significant role of individual agency in the interplay between personal experiences and the broader Thai social structure in negotiating identities. The implications for researchers and practitioners focus on the potential of Global Englishes classrooms as a locus for positive identity construction and the importance of considering differing perspectives to create a more nuanced understanding of identity and participation in L2 learning. The study also suggests a bottom-up pedagogical approach with ELF-oriented materials for learners to develop more favourable identities as English as a lingua franca users and/or multicompetent speakers.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.010 | 0.007 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.007 |
| 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".