Contemporary qualitative research methods in sport management
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Over the past 10 years, qualitative research methods have become more commonplace in sport management scholarship. In the main, they are largely defined by a formulaic approach in which case studies, semi-structured interviews, and coding are often used. Alternative qualitative approaches, which may open up research to new audiences and research participants, and challenge assumptions about ‘good research, ’ appear to be largely absent. This special issue on contemporary qualitative research methods in sport management includes conceptual advances in community-based research approaches, Indigenous methodologies, participatory action research, autoethnographies, and narratives. In addition, we present empirical papers that illustrate the use of autoethnography, narrative, digital ethnography, and phenomenology in the field. These articles provide examples for use in classes on qualitative research methods, and can serve to inspire others to use contemporary methods. We encourage sport management researchers to learn about and use contemporary qualitative data collection and analysis, and alternative means of disseminating their work to further enhance the field and challenge ways of knowing and doing research.
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.
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 | 0.031 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 it