Be mindful of the future: information and knowledge management in Star Wars tie-in fiction
Bibliographic record
Abstract
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.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.010 | 0.009 |
| Scholarly communication | 0.011 | 0.007 |
| Open science | 0.000 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.010 | 0.001 |
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".