Construction and Negotiation of Identities of NNES Graduate Students in an ESL Context
Bibliographic record
Abstract
This study critically investigates construction and negotiation of multiple and often hybrid identities of non-native English speaking (NNES) graduate students majoring in an English education program at a Canadian university. With a theoretical framework of language socialization (LS) with critical discourse analysis (CDA) and communities of practice (CoPs) approaches, this multiple case study examines how NNES graduate students construct, reconceptualize and negotiate their identities through discourse in their LS processes. Findings suggest that NNES graduate students often shuttled between various CoPs and constructed multiple and hybrid identities which are non-static, situational as well as contextual. Therefore, indiscriminative and uniformed construction of identity as NNES should be discouraged as it could be misleading and has a risk of grouping and essentializing NNES as novice and a marginalized member of academic CoPs. Moreover, NNES graduate students can be empowered and make the best of their investments by perceiving themselves as individuals who are linguistically and socioculturally multicompetent members of various academic CoPs while recognizing their multiple and hybrid identities.
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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 | 0.010 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.020 | 0.022 |
| Scholarly communication | 0.009 | 0.005 |
| Open science | 0.002 | 0.016 |
| Research integrity | 0.002 | 0.003 |
| 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".