Bibliographic record
Abstract
Teaching courses on religion and popular culture in a Canadian academic institution has provided me with significant challenges. According to Raymond Williams (1983), “popular” can have at least four meanings in common parlance. (1) It can mean that which is well-liked by a lot of people ( e.g., the top ten bestselling books); (2) it can mean that which is inferior to elite or high culture (e.g., pop music versus opera); (3) it can mean that which deliberately tries to win the favour of “the people” (e.g., political campaigns); or (4) it can mean that which is made by the people (e.g., youtube). Some of these definitions lead to an assumption that popular culture is not particularly “deep” or meaningful. For some people then, the question is, as David Chidester asks in his book Authentic Fakes, “[h]ow does the serious work of religion, which engages the transcendent, the sacred, and the ultimate meaning of human life in the face of death, relate to the comparatively frivolous play of popular culture?” The Journal of Religion and Popular Culture has consistently shown the multiple ways this serious work of religion relates to popular culture.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.007 |
| Science and technology studies | 0.037 | 0.019 |
| Scholarly communication | 0.015 | 0.005 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.004 | 0.007 |
| Insufficient payload (model declined to judge) | 0.008 | 0.000 |
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.
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".