“They’re Different From Who I Am”: Making Relevant Identities in the Middle Through Talk-in-Interaction
Bibliographic record
Abstract
This qualitative study builds on earlier research on language and identity by focusing on how Canadian Generation 1.5 university students enact their identities through talk-in-interaction. Drawing on (applied) Conversational Analysis (CA) to analyze critically the production and management of social institutions in talkin-interaction in tandem with Membership Categorization Analysis (MCA) to examine the cultural resources individuals draw on to describe, identify, or make reference to other people and themselves, I undertake a critical discourse analysis (CDA) of data from semistructured interviews with four Generation 1.5 students conducted in a large, public, English-medium university in British Columbia. Rather than approaching the interview as a neutral technology that seeks to discover “truths,” I theorize the interviews as meaning-making ventures in themselves, adopting a reflexive orientation that recognizes that data are situated representations co-constructed through interaction with the interviewer. The study reports on how these students, in response to the interactionally occasioned constraints “inhabiting” our talk, produced identities that aligned with select “scholarly representations” from the applied linguistics literature that casts Generation 1.5 students in the middle. The study reveals how identity, power, and social issues are produced and managed in talk-in-interaction and how insights from M/CA might address matters of social justice in educational contexts.
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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 | 0.011 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.032 | 0.030 |
| Scholarly communication | 0.012 | 0.008 |
| Open science | 0.003 | 0.012 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".