28. The Nose Knows: A Sensual Analysis of Paradise Lost and His Dark Materials
Bibliographic record
Abstract
Both John Milton’s Paradise Lost and Philip Pullman’s retelling of the story, His Dark Materials, use scent imagery in descriptions of morally significant characters. But what is the significance of the consistent scent imagery? Hans J. Rindisbacher’s theory in The Smell of Books: A Cultural-Historical Study of Olfactory Perception in Literature (1992) states that scent imagery, in literature pertaining to Christian symbolism, indicates a dichotomy of morality. This theory is modified to illustrate that scent is used to signify a character’s condition of knowledge. The presence of good scent in Paradise Lost indicates that a character possesses pure knowledge of good and evil, the presence of bad scent indicates a character’s corrupt condition of knowledge of good and evil. Alternatively, the presence of good scent in His Dark Materials signifies that a character possesses pure self-knowledge, whereas the presence of bad scent indicates the corruption of a character’s self-knowledge. Furthermore, the attribution of neutral scent or lack of scent imagery to morally significant characters, such as Satan and Mrs. Coulter, signifies the morally ambiguous actions which make these characters difficult to define as virtuous or evil. The analysis of the scent imagery in Paradise Lost and His Dark Materials demonstrates the significance of scent theory in the research and analysis of literature, as a possible method of evaluating a character’s moral status.
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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 | 0.002 | 0.001 |
| Science and technology studies | 0.006 | 0.014 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 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".