The Discursive Management of Emotionality in the L2 Research Interview.
Bibliographic record
Abstract
This study investigates the ways in which emotions and emotionality are managed as topics and resources in second language (L2) research interviews. A continued challenge for researchers is how to define and operationalize emotions and other putative psychological phenomena. One popular research methodology that treats emotions as the object and product of inquiry is the qualitative interview, where emotions are investigated as initiators, inhibitors, or outcomes of language-related activity. However, a growing criticism of interview research is that by elevating the thematic and dramatic content of the talk we ignore the methodic interactional practices by which the data are produced. Data are drawn from 30 hours of face-to-face interviews with adult immigrants from Vietnam, Cambodia, and the Philippines living in the US and Canada. Using the methodology of conversation analysis, and informed by ethnomethodology and discursive psychology, I examine how emotions are invoked, represented, and made procedurally consequential in interview interaction. Three specific interactional resources are examined: (a) emotion story prefaces used by interviewees to project emotion-implicative stories fitting the interview agenda, (b) interviewer questioning sequences and their function in eliciting interview talk of emotions and emotional experiences, and (c) emotion reformulations and how particular emotion-indexing terms are used to offer, take up, reject, and scale various descriptions. Talk of negative emotions (e.g., anger, sadness, shame) and experiences (e.g., problems, complaints, discrimination) was found to be particularly salient in the data. One explanation is that this is what is cooperatively treated as memorable, tellable, and expected in research interviews and autobiographic talk. This study further demonstrates that a discursive approach to emotions, employing a conversation analytic methodology, offers a systematic and empirical means to analyze, rather than summarize or speculate about emotions and emotional talk. It also allows a careful methodological and analytical critique of our research processes.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 teacher head, 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".