Bibliographic record
Abstract
In Thomas King’s novel Green Grass, Running Water , Dr. J. Hovaugh, head of a mental hospital in Florida, crosses the border into Canada in search of four old Indians who have escaped from his institution. He is accompanied by his black janitor, Miss Babo Jones, and just as “J. Hovaugh” sounds conspicuously like “Jehovah” (the character thus named bearing obvious delusions of god-like omnipotence), Babo is named after the black slave who leads the revolt on the slave vessel in Herman Melville’s novella Benito Cereno . 1 When the unlikely couple approaches the Canadian border, Babo first notices that the flagpoles at both border stations are “crooked”: the one near the Canadian border station “fell slightly to the left,” whereas the American flagpole “leans a bit to the right” (T. King 1993a, 236). This first implicit commentary toward the respective countries’ political inclinations—Canada the more liberal, the United States the more conservative of the two 2 —is followed by the description of the actual border crossing, that is, the encounter with the Canadian border guard, who, ignoring Babo, asks Hovaugh: “Are you bringing anything to Canada that you plan to sell or leave as a gift?” … “Nothing,” said Dr. Hovaugh. “What about her?” said the guard. “She’s with me.” “Nonetheless you’ll have to register her,” said the guard. “I see,” said Dr. Hovaugh. “All personal property has to be registered.” “Yes,” said Dr. Hovaugh. “Of course.” “It’s for your protection as well as ours,” said the guard. Babo looked back at the American border station and then at the Canadian border station. “Where did you say we were?” she said. “Welcome to Canada,” said the guard, and she handed Dr. Hovaugh her clipboard. “Sign here,” she said, “and here.” “Thank you,” said Dr. Hovaugh. “Have a nice day,” said the guard. (T. King 1993a, 236–37)
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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 | 0.004 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.007 | 0.012 |
| Science and technology studies | 0.032 | 0.011 |
| Scholarly communication | 0.007 | 0.003 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".