Formulating and scaling emotionality in L2 qualitative research interviews
Bibliographic record
Abstract
From a corpus of ‘troubles-tellings’ (Jefferson 1988) generated in qualitative research interviews with L2 (second language) English-speaking adult immigrants in the US and Canada, this case study examines how formulation and intensification, supported by various linguistic and paralinguistic resources, enable story teller (interviewee) and story recipient (interviewer) to intersubjectively categorize and manage the affect-laden descriptions of people and events set within particular institutional, interactional, psychological, and moral worlds. As a result, emotionality is shown to be more than an outcome of L2 users’ sociolinguistic experiences but a series of highly coordinated actions that progress the interview activity and make accountable as well as account for a complex network of social conduct and categorial 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 | 0.072 | 0.089 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.007 |
| Science and technology studies | 0.007 | 0.017 |
| Scholarly communication | 0.010 | 0.008 |
| Open science | 0.003 | 0.012 |
| Research integrity | 0.002 | 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".