Bibliographic record
Abstract
IF THERE IS ANY popular culture phenomenon that can be referred to as “religion,” it would be the fandom associated with the Star Wars films. In the 2001 census in many English-speaking countries, a number of people identified their religion as “Jediism,” including 70,000 in Australia, 21,000 in Canada, 53,000 in New Zealand, and 390,127 in England and Wales (“Jedi Census” 2012). This may well have begun as a joke (Emery 2001), but it is also clear that at least some of those who support this movement take it seriously, such as the online Jedi Church (“Jedi Church” 2012). More significant, perhaps, is the number of fan activities related to Star Wars which might express some of the “markers” of religion, such as communal identity, a system of beliefs and values, myths and ritual practices. One cannot attribute all of these to marketing, as a number of fan activities clearly do not originate from corporations such as Lucasfilm but are generated by the fans themselves. Although it is hard to track all of this, one can find a sampling in fan-made videos posted on youtube.com and other web sites, fan stories based on Star Wars categories, fan art and comic books, fan-designed games, costume events (including weddings), and various artifact recreations (Figure 1).1 The interactive nature of the internet has allowed these activities to develop in unforeseen ways, as it is possible to easily post content that can be edited by users around the world who can interact in real time, and who can network in ways that formerly would have been impossible.
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 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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.010 | 0.012 |
| Scholarly communication | 0.012 | 0.007 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.014 | 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".