Bibliographic record
Abstract
One of the greatest storytellers of modern times, Mark Twain, remarked that “there are several kinds of stories, but only one difficult kind - the humorous.” He differentiated the humorous story, which he claimed to be truly American, from the comic story and the witty story, which he classified as respectively English and French. Like Twain, and like certain Canadian writers who preceded or followed him, Margaret Atwood anchors her playful writing in the motifs and mindset of North America. While her novels, stories, and short fictions can be poetic, biting, or even grim, they are almost invariably suffused with the humor that Twain identified as being indissociable with the manner of the telling, as opposed to the comic and the witty story which rely on the matter (Twain, How to Tell a Story , p. 7). Also investigating the mechanisms of humorous writing, Atwood herself has classified it into three commonly acknowledged genres: parody, satire, and “humor” (although her writing thoroughly blurs these artificial boundaries). In the characteristic way of postcolonial writers promoting their distinctive national culture, she has set out to identify British and American humor and distinguish Canadian humor from the two metropolitan forms. Yet the discrete dimension of Canadian humor in her analysis rests not on techniques of production, but on notions of reception, or the complex relations between what she terms the “laugher,” the “audience,” and the “laughee” ( SW , p. 175).
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.009 | 0.005 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".