Collaborative Self-Ethnography: Navigating Self-Reflexivity in a Sport Management Context
Bibliographic record
Abstract
The main goal of our article is to encourage personal reflection within the field of sport management as a tool to strengthen methodological approaches in our research. We explore and discuss the utility of collaborative self-ethnography as one way to acknowledge personal identities through a reflexive account of our experiences as sport fans and sport researchers with this methodology. We draw on a previous study of our experiences as sport fans to illustrate techniques, downfalls, and benefits of studying one’s experiences in a collaborative methodological approach. We have two objectives: First, we hope to encourage sport management researchers to acknowledge and reflect on their personal identities related to sport, such as being a fan, coach, volunteer, or former participant, in their research. Second, we aim to demonstrate the utility of collaborative self-ethnography as one way to incorporate reflexivity in sport management research and theory development.
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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 | 0.042 | 0.038 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.008 | 0.016 |
| Scholarly communication | 0.007 | 0.010 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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.
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".