Apocalyptic cinema: the supernatural T.V. series as an original neo-humanistic interpretation of the Book of Revelation
Bibliographic record
Abstract
This thesis aims to develop a critical analysis and interpretation of a long-running streaming television series, Supernatural (2005-2020), whose main story is adapted from the Book of Revelation. This thesis describes, analyzes, and interprets the creatively unique apocalyptic worldview presented in the series through a content/narrative analysis methodology and employing the modeling technique of Game Theory. This study shows that Supernatural creates a universe in which an apocalyptic worldview is projected by a radical type of humanism, which I term ‘Neo-Humanism.’ Neo-Humanism is a kind of perspective whose worldview has roots in the deepest layers of religious belief and its lifestyle is the same as the most exclusive levels of humanism. Neo-Humanism claims that if there is a god, who created human beings with free will and the ability to distinguish between good and evil, as theism says, His creation will inevitably be above everything and able to do everything. And more importantly, even the Creator God Himself does not want, cannot, and should not interfere in humans’ affairs. Even though the Neo-Humanism perspective seems too illogical and surreal to be believable, this thesis shows that in the universe that Supernatural depicts, the inner logic of Neo-Humanism works appropriately.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.011 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".