From Grierson to the docu-soap : breaking the boundaries
Bibliographic record
Abstract
IntroductionPart I: Grierson's legacyGrierson in Canada: The peak of his creative career? - Ian Lockerbie Stanley Hawes: A 'Grierson man' in Australia - Ina Bertand Crossing the cattle drive: Grierson and Australia - Deane Williams John Grierson in South Africa: Misunderstanding apartheid - Keyan G. Tomaselli Poru Ruta: Paul Rotha and the politics of translation - Abe Mark Nornes Germaine Dulac and French documentary film-making in the 1930s - Si n Reynolds Grierson and the public relations industry in Britain - Jacquie L'EtangPart II: The changing culture of British televisionBreaking the boundaries - John Willis Trailing documentaries - Peter Meech The docu-soap: A critical assessment - Richard Kilborn Keeping track of the locals: Governance, television and the community - Gareth Palmer Fearful and safe: Audience response to British reality programming 'You either believe it or you don't ...': Television documentary and pseudo-science - Vincent Campbell Access, authorship and voice: The emergence of community programming at the BBC - Sylvia Harvey Through the eyes of the Video Nation - Mandy Rose Towards the defining digital documentary - Brent Macgregor with Roddy Simpson Disclaimers, denials and direct address: Captioning in docudrama - Derek Paget Making connections: The European Convention on Human Rights, the Independent Television Commission and the documentary - Brian Winston Documentary and the public sphere - Michael Chanan
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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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.020 | 0.021 |
| Scholarly communication | 0.016 | 0.008 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.004 | 0.009 |
| Insufficient payload (model declined to judge) | 0.027 | 0.005 |
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".