Emotional scholar-fandoms: negotiating researcher identity in a study with reality show participants
Bibliographic record
Abstract
This article analyses the research practice of interviewing media participants by critically reflecting on the process of interviewing former reality show contestants. I argue that interviewing was not a straightforward process but rather a deeply emotional one. The interview process complicated my scholar-fan identity, my feelings about the research process and my reality TV fandom. It was only through a process of self-reflexive analysis and research practice that I could successfully negotiate these two identities. In the following article, I show how I came to adopt this self-reflexive position, and reflect on how this position could better inform future research in celebrity studies. In addition, this paper contributes to the rich body of scholarship on interview research methodologies by emphasizing the key role that interviewing has to play in shaping the relationships between celebrities, fans and academics.
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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 | 0.056 | 0.077 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.020 | 0.041 |
| Scholarly communication | 0.016 | 0.010 |
| Open science | 0.003 | 0.017 |
| Research integrity | 0.004 | 0.005 |
| 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".