Bibliographic record
Abstract
In recent years, religious undertones have permeated American popular culture. Television, movies, and books are all drawn to religious themes such as Angels, The Rapture, and the battle between Heaven and Hell. While the fight between good and evil is a popular theme, many religious based mediums have very different goals. For example, the television show Supernatural was created to entertain people, specifically millenials. Supernatural portrays characters that face the same questions that many millennials face when it comes to religion, which allows the television series to remain entertaining while grappling with many religious themes. While Supernatural portrays many religious themes with the goal to entertain, the Left Behind book series is a guide for those who are preparing for The Rapture. This book series contains very prominent themes about evangelical Christianity. Even though each of these mediums have very different agendas, they also have some similarities. Both Supernatural and Left Behind have themes of violence that play to an overarching narrative between good and evil. Additionally, they both create a hierarchy of religions with Christianity at the top. Both simultaneously have a large amount of action-packed, violent scenes to draw in the consumer as well as religious imagery that makes Christianity seem to be good and all other religions seem to be evil. Even though the Left Behind series and the television show Supernatural seem completely different, with different audiences and different goals, they both have the two overarching themes towards violence and placing Christianity at the top of the hierarchy of religions by playing into the good versus evil narrative with Christianity being the good, and all other religions being the evil.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.005 | 0.008 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 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".