Bibliographic record
Abstract
The focus ofDeniseMann’s previous chapter on contemporary television authorship in connection with the Lost (2004-) franchise identifies and questions an emerging model of production and marketing that applies to a number of recent big-budget US drama series. At a time when “must see” television has become crucial to the branding strategies of TV channels in the US and beyond, together with the competitive struggles to both retain and acquire audiences in a multichannel and multiplatform era, I am prompted to offer comparative comment from a UK perspective.While buying in a big-budget, US-produced drama series/serial has certainly operated to shore up the corporate identity of UK channels,1 high-end, home or co-produced dramas are increasingly adopting and adapting the kind of US model outlined in Mann’s chapter. Perhaps the most obvious example of this trend in British television can be witnessed in the BBC, BBC Wales, and the Canadian Broadcasting Company’s big-budget relaunch and end of DoctorWho (2005-2008), currently at the finish of the first run of its fourth season.2
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.008 | 0.011 |
| Scholarly communication | 0.007 | 0.009 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.008 | 0.012 |
| Insufficient payload (model declined to judge) | 0.009 | 0.002 |
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".