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Record W2395414926 · doi:10.1386/jfs.4.1.105_1

Making global audiences for a Hollywood ‘blockbuster’ feature film: Marketability, playability and The Hobbit: An Unexpected Journey (2012)

2016· article· en· W2395414926 on OpenAlexaff
Charles H. Davis, Carolyn Michelle, Ann Hardy, Craig Hight

Bibliographic record

VenueThe Journal of Fandom Studies · 2016
Typearticle
Languageen
FieldSocial Sciences
TopicDigital Marketing and Social Media
Canadian institutionsToronto Metropolitan University
Fundersnot available
KeywordsHollywoodDisappointmentFantasyAdvertisingFilm genreAnticipation (artificial intelligence)Feature filmVisual artsArtPsychologyComputer scienceMovie theaterLiteratureSocial psychologyArt history

Abstract

fetched live from OpenAlex

Abstract This article interprets two key concepts in movie marketing (marketability and playability) through an empirical examination of the effects of commercial interpellation of audiences for a Hollywood ‘blockbuster’ fantasy film, Peter Jackson’s The Hobbit: An Unexpected Journey (2012). The article reports results of two online surveys of Hobbit audiences, one in November 2012 in the weeks preceding theatrical release, and one in February–June 2013 among post-viewing audiences, employing a mixedmethods approach that includes Q sorting and a questionnaire. We identify and describe five main pre-release and five main post-viewing audience groups, showing that the film had greater marketability than playability. Three of the pre-release audience groups expressed a high degree of anticipation to see the film, but only one post-viewing audience group expressed a high degree of enjoyment, while the others expressed various degrees of disappointment. We discuss the attributes of the film that most affected the film’s marketability and playability for each of the audience groups during the interpellation process from prefiguration to reception.

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)0.010
metaresearch head score (Gemma)0.016
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Science and technology studies
Consensus categoriesScience and technology studies
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.282
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0100.016
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0020.003
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.077
GPT teacher head0.394
Teacher spread0.317 · 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 designQualitative
Domainnot available
GenreEmpirical

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

Citations6
Published2016
Admission routes1
Has abstractyes

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