Repair as a Conversational Resource for (Dis)Affiliation in the Negotiation of Linguistic Identity
Bibliographic record
Abstract
This article focuses on the negotiation of membership to and the linguistic boundaries of a minority French-Canadian community. It analyses the expression of linguistic identity as a situated accomplishment by means of interactional resources, and of repair in particular. In each excerpt, repair is used by the co-participants to manage affiliative or disaffiliative stances towards their own and others' linguistic categories and identities. In doing so, they engage in categorisations along many dimensions of linguistic identities: 1) the ill-defined boundaries of 'jrancophonie', 2) the dividing lines between French and English, and 3) claimed access to the vernacular and the linguistic competence it entails. The data analysis reveals the malleable and negotiable nature of identity; it also underlines the role of conversational and linguistiC resources for articulating a sense of self around social constructs such as Francophone, Anglophone, bilingual speaker, language learner, and competent speaker of French.
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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 | 0.006 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.009 | 0.013 |
| Scholarly communication | 0.008 | 0.005 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.001 |
| 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".