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Record W2965426990 · doi:10.4324/9780203879597-14

Showrunning the Doctor Who Franchise:A Response to

2009· book-chapter· en· W2965426990 on OpenAlexaboutno aff
Denise Mann CHRISTINE CORNEA

Bibliographic record

Venuenot available
Typebook-chapter
Languageen
FieldSocial Sciences
TopicArtificial Intelligence in Law
Canadian institutionsnot available
Fundersnot available
KeywordsFranchisePsychologyBusinessMarketing

Abstract

fetched live from OpenAlex

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.907
Threshold uncertainty score0.996

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.001
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0120.004

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.

Opus teacher head0.064
GPT teacher head0.358
Teacher spread0.294 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; both teacher heads agree on what is shown here.

Study designNot applicable
Domainnot available
GenreOther

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".

Quick stats

Citations0
Published2009
Admission routes1
Has abstractyes

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