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Record W4403436694 · doi:10.1080/2159676x.2024.2416234

Research on the run: a carnal sociological approach to running interviews

2024· article· en· W4403436694 on OpenAlexafffund
Stéphanie Bogue Kerr

Bibliographic record

VenueQualitative Research in Sport Exercise and Health · 2024
Typearticle
Languageen
FieldHealth Professions
TopicHomelessness and Social Issues
Canadian institutionsUniversity of Ottawa
FundersSocial Sciences and Humanities Research Council of Canada
KeywordsSociologySociological researchEpistemologyPsychologySocial sciencePhilosophy

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.089
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Science and technology studies, Research integrity
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.343
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0890.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.002
Science and technology studies0.0020.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.005
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.746
GPT teacher head0.706
Teacher spread0.040 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designQualitative
Domainnot available
GenreEmpirical

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".

Quick stats

Citations7
Published2024
Admission routes2
Has abstractyes

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