Research on the run: a carnal sociological approach to running interviews
Bibliographic record
Abstract
In recent years, there has been increased interest to the ways people move about in their everyday lives. This interest has given rise to the use of novel forms of mobile interview methods. While increased attention to walking interviews has supported the emergence of different methodologies, there is comparatively little methodological discussion on running interviews. This article stems from 22 running interviews, conducted in the context of a carnal sociological study that sought to gain insight into the embodied processes of 11 individuals who had integrated running into their processes of recovery from substance use. While carnal sociology is usually applied to ethnographic research, this study retained the embodied and embedded qualities that Wacquant (2015) considered vital to his theory through the engagement of the researcher’s own body as a methodological tool. This article aims to illustrate how carnal sociology may be applied to mobile methods, with theory and method converging to shape the structure of the interviews, the establishment of rapport with participants, the technological choices, and the safeguarding of the runner-researcher habitus. Finally, the strengths and limitations of running interviews as a means of data collection are discussed.
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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 | 0.053 | 0.037 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.008 | 0.008 |
| Science and technology studies | 0.018 | 0.036 |
| Scholarly communication | 0.009 | 0.010 |
| Open science | 0.003 | 0.012 |
| Research integrity | 0.002 | 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".