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Record W2605949022 · doi:10.5931/djim.v13i1.6925

Be mindful of the future: information and knowledge management in Star Wars tie-in fiction

2017· article· en· W2605949022 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
venuePublished in a venue whose home country is Canada.

Bibliographic record

VenueDalhousie Journal of Interdisciplinary Management · 2017
Typearticle
Languageen
FieldSocial Sciences
TopicDigital Games and Media
Canadian institutionsDalhousie University
Fundersnot available
KeywordsStorytellingFranchiseStar (game theory)SituatedNon-fictionTransition (genetics)SociologyHistoryComputer scienceLiteratureBusinessNarrativeArtMarketing

Abstract

fetched live from OpenAlex

In the last fifty years, media franchises have been using tie-in fiction to expand their universes and tell stories outside main events. This paper examines how information is used specifically in Star Wars tie-in fiction and its recent transition to using knowledge management. To start, this paper looks at the history of tie-in fiction from its roots in the 1960s to the modern day, before transitioning to the role of brand managers and editors as information managers. Then, this paper documents the history of Star Wars tie-in fiction and how information strategies were implemented through 2014 and how it impacted the franchise’s canon. Finally, this paper examines the recent move towards a unified canon and how this shift towards knowledge management has impacted storytelling. This paper concludes that while it is too early to evaluate its results, Star Wars was uniquely situated among franchises to move towards knowledge management through its prior information management efforts.

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.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.919
Threshold uncertainty score0.271

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0010.001
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 head0.013
GPT teacher head0.308
Teacher spread0.295 · 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