It Depends Who You Are: On Asking Difficult Questions in Leisure Research
Bibliographic record
Abstract
We know that social researchers are not independent of the research process. In this paper, we use research on the potentially very sensitive subjects of identity and racism to reflect on the significance of the interaction of the researcher’s own cultural biography with that of the participant. Awareness of that is an important step in devising procedures of communication and interpretation. The paper addresses the associated challenges involved in the various stages of initial engagement with respondents/participants, data gathering and analysis. In doing so, it argues the importance of interrogating our own inter operations just as we review critically the accounts of respondents. The paper offers some indicators of how an awareness of the complexities of working in sensitively charged areas can suggest a way forward. Perhaps because of the sensitivity of the subject areas of this research such issues are easier to recognise here, but they apply to social research more generally.
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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 | 0.113 | 0.200 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.024 | 0.070 |
| Scholarly communication | 0.025 | 0.036 |
| Open science | 0.003 | 0.017 |
| Research integrity | 0.012 | 0.021 |
| Insufficient payload (model declined to judge) | 0.008 | 0.006 |
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".