Making a Scientist: Discursive "Doing" of Identity and Self-Presentation During Research Interviews
Bibliographic record
Abstract
Participating in an interview is taking part in an activity system that is often very different from the daily lives of most individuals. Grounding ourselves in an activity theoretic perspective, we regard the interview event and who or what these agents become during that process as an outcome of the activity of "doing interviews." In contrast to the modern concept of identity, a stable and characteristic feature of an individual, we understand identity as arising from social interactions—identity and activity are said to be in a dialectical relationship. Interviews are thus occasions whereby identity and issues of self-presentation have to be managed by agents primarily through discourse processes. By further regarding interviews as "topic" in this article we make salient their co-constructive nature qua social interaction rather than as a neutral data gathering tool. Our case study of an interview with a renowned environmental scientist demonstrates how identity and issues of self-presentation were discursively played out using the concepts of "stake" and "footing." It was found that our participant came to be a full-fledged member of the scientific community with traits typically ascribed to scientists such as expertise, objectivity, passion and disinterestedness. This discursive "doing" of identity and self-presentation during research interviews is a pervasive effect and cautions practitioners against treating interviews as an unproblematic methodology. URN: urn:nbn:de:0114-fqs0401123
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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 | 0.129 | 0.155 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.016 | 0.047 |
| Scholarly communication | 0.013 | 0.013 |
| Open science | 0.004 | 0.016 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.003 | 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".