Bibliographic record
Abstract
PART ONE: INTRODUCTION Media Organization and Production - Simon Cottle Mapping the Field PART TWO: GLOBAL CORPORATIONS, LOCAL ALTERNATIVES Corporate Media, Global Capitalism - Robert W McChesney Organization and Production in Alternative Media - Chris Atton PART THREE: CORPORATE CHANGE AND ORGANIZATIONAL CULTURES Strategizing Technological Innovation - Timothy Marjoribanks The Case of News Corporation Organizational Culture inside the BBC and CNN - Lucy K[um]ung-Shankleman PART FOUR: PRODUCERS PRACTICES AND THE PRODUCTION OF CULTURAL FORMS The Brains Trust - Paddy Scannell An Historical Study of the Management of Liveness on Radio Journalists With a Difference - Eamonn Forde Producing Music Journalism Cultures of Production - Julian Matthews The Making of Children's News PART FIVE: CHANGING INTERNATIONAL GENRES AND PRODUCTION ECOLOGIES International TV and Film Co-Production - Doris Baltruschat A Canadian Case Study Producing Nature(s) - Simon Cottle The Changing Production Ecology of Natural History TV
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.009 | 0.009 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.054 | 0.010 |
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".