Bibliographic record
Abstract
Fan studies has a long tradition of framing fandom as active, creative, and participatory. Yet, scholarship on film and television fans has primarily investigated fandom and fan practices in relation to the consumption, production, and criticism of fictional texts. In turn, non-fiction texts and fan practices have found considerably less attention. However, as this dissertation demonstrates, fans are active and creative participants in assembling, preserving, restoring, and disseminating materials from the past and in transforming these materials into print and online publications, podcasts, video tutorials, documentaries, and museum exhibitions. Drawing from the field of public history and the idea of a “participatory historical culture,” this dissertation conceptualizes and examines fans as producers and distributors of historical knowledge through the textual analysis of a wide range of fan-made histories of the Star Wars franchise. This dissertation foregrounds practices, objects, and networks that so far have found little attention in fan studies: the distinct forms of historical media fans produce; community structures and hierarchies with historians and history-making at their centre; fan historians’ relationship to the media industries; fan contribution to cultural heritage initiatives; the impact of fan labor in specific local contexts and beyond the media industries. As such, this dissertation shows how history-making is central to the formation, maintenance, and shaping of individual and collective fan identities and memories.
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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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.009 | 0.014 |
| Scholarly communication | 0.008 | 0.005 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 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".