Bibliographic record
Abstract
As an archetype, the vampire is alive and well in the collective psyche. A closer look can reflect back to us what we deem monstrous out there as well as inform us about the monstrous within. This is fundamental to Jung’s notion of the Shadow and fundamentally an issue of ethics. This paper explores how specific attributes of the contemporary vampire reflect our ethical agon at the beginning of the 21st century, using two popular vampire sagas, the Twilight series and True Blood as examples of the tensions between abstinence and indulgence among a predatory species. This paper explains the elements of the female Bildungsroman literary genre found in both stories, which offers psychologists a particularly fruitful view into ethics and character development, and shows how the central love relationship between a human female and a vampire male dramatizes some of the trickier aspects of relating to the Other in the most intimate manner. The paper concludes by comparing Aristotelian virtue ethics with Jung’s notion of individuation to discern who is the real monster—and who aspires to the classical notion of arête.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.023 |
| Scholarly communication | 0.005 | 0.007 |
| Open science | 0.001 | 0.004 |
| 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".