Contemporary digital qualitative research in sport, exercise and health: introduction
Bibliographic record
Abstract
This paper provides an introduction to the Special Issue on Digital Qualitative Research in Sport, Exercise and Health. The aim is to spur qualitative researchers to new ways of thinking, new ways of doing, and new ways of representing with the ultimate goal of supporting new ways of knowing, through the lens of digital technologies. First, digital qualitative research is defined and articulated as research that engages in qualitative inquiry and meaning making through digital content, digital contexts and/or digital practices. In using this definition, an analysis of the articles published in sport, exercise and health reveal that most research to date has primarily focused on technology as method, the impacts of technologies on participants, technology as an empirical finding, and/or technology as a medium to represent research findings. Accordingly, and with the intent of advancing digital qualitative research in sport, exercise and health, the concept of practice architectures is used as a heuristic device to articulate the cultural, social and material conditions that potentially support or constrain current and potential future research. Embedded in this discussion, is an overview of the papers in this Special Issue. Overall, these papers showcase the most innovative and world-leading digital qualitative research in sport, exercise and health to date, and provide inspiration and direction for moving forward. The papers use established qualitative concepts, theories and methodologies, offer challenges to existing frameworks, and illustrate contemporary understandings of sport, exercise and health through digital mediums.
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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 | 0.063 | 0.066 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.010 |
| Science and technology studies | 0.004 | 0.014 |
| Scholarly communication | 0.012 | 0.012 |
| Open science | 0.003 | 0.010 |
| Research integrity | 0.006 | 0.006 |
| Insufficient payload (model declined to judge) | 0.015 | 0.003 |
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".