Bibliographic record
Abstract
Elizabeth Ruth’s second novel Smoke (2005) does not fit the generic category of crime fiction per se ; rather, her work is representative of a growing number of queer fictions that self-consciously play upon the multiple resonances of the outlaw. Making extensive use of the hermeneutic code, the text operates much like detective fiction, accumulating clues for the reader and the protagonist to decipher. Cultural insiders who understand the rules of the game will recognise precisely what type of outlaw is at stake at a relatively early stage. Set in the village of Smoke in the tobacco-growing region of south-western Ontario in 1958–1959, the narrative revolves around teenaged protagonist Buster McFiddie, disfigured for life in a fire, and the ageing Doctor John Gray, who becomes Buster’s mentor. To distract Buster from the physical and psychological pain of his injuries, Doc John tells him thrilling stories about the Purple Gang and other Detroit mobsters that he claims to recall from his youth in the 1920s and early 1930s. When their region becomes the target for a series of daring daylight robberies, some villagers suspect Buster is the culprit, and Buster in turn suspects the doctor. And there is no smoke without fire, although Doc John’s secret is not quite what Buster expects. The text is coded to be legible to the attentive reader, long before Buster uncovers the evidence for himself: John Gray, happily married to Alice for almost 25 years, is transgendered 1 — which in this particular historical time and place “makes him a wanted man” (Ruth 2005: 253). These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
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 | 0.000 | 0.000 |
| Science and technology studies | 0.020 | 0.015 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.004 | 0.009 |
| Insufficient payload (model declined to judge) | 0.005 | 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".