Who's Afraid of Susan Sontag? or, the Myths and Metaphors of Cancer Reconsidered
Bibliographic record
Abstract
Susan Sontag's book, Illness as Metaphor, has framed our understanding of the relationship between disease metaphors and illness experiences in modern Western society. Her view that metaphors can render diseases socially as well as physically mortifying has influenced a generation of scholars: her conclusion that cancer sufferers are shamed and silenced by metaphors has likewise shaped public perception of neoplastic diseases. Despite the eloquence of Sontag's prose and the force of her convictions, her conclusions are not wholly persuasive. Some scholars have critiqued her faith in the power of science to dispel the myths and metaphors of disease; others have pointed out that it is neither desirable nor possible to strip illness of its symbolic meanings. It has been my purpose to test Sontag's assumptions about the impact of cancer metaphors, to weigh her arguments against the experiences and attitudes embodied in patient correspondence, obituaries and death notices, medical and educational literature, and fiction. Popular and professional reactions to neoplastic diseases in both Canada and the United States during the first half of the twentieth century reveal that, while many North Americans regarded cancer as a dreadful affliction, the disease did not, as Sontag has argued, predictably reduce them to a state of silence or disgrace.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.021 |
| Scholarly communication | 0.006 | 0.013 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.006 | 0.008 |
| Insufficient payload (model declined to judge) | 0.003 | 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".