Bibliographic record
Abstract
This chapter proposes methods for conducting production enquiry at a strained time in the UK media sphere. Whilst scholars mused about whether the Leveson Report and the phone-hacking crimes that preceded it might encourage greater transparency among private news organizations, I argue from experience that public institutions – heretofore perceived as being slightly more open to social research – became reticent in the wake of contemporary problems, not least of which was the scandal concerning former presenter Jimmy Savile. After briefly narrating the challenges I encountered during qualitative fieldwork in Glasgow, I describe the strategies I employed to surmount the challenges and complete the research. The strategy I focus on in this chapter is the inclusion of the ethnographic self as resource. I ground this methodological approach in recent anthropological writings from Collins and Gallinat (2010), who consider it imperative for their discipline. More than reflexivity but less than autobiography, this approach invites researchers to consider themselves among their informants, negotiating access and building rapport but also contributing data and providing a filter through which data are analyzed. I consider the approach with critical reference to examples from literature on media production. I then integrate my prior professional experience as a broadcast journalist in Canada with my research on relationships between journalists and Muslim sources in Scotland. This becomes a template for media researchers with valuable professional experience to make the most of that asset, being bold enough to include it and cautious enough to account for it in a rigorous manner. In this way, anthropological theory continues to advance the ethnographic study of media production.
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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 | 0.062 | 0.052 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.009 | 0.008 |
| Science and technology studies | 0.013 | 0.073 |
| Scholarly communication | 0.020 | 0.031 |
| Open science | 0.004 | 0.018 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.006 | 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 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".