Bibliographic record
Abstract
In 1965, recognizing that publishers had released relatively few short story collections during the previous decade, the Literary History of Canada predicted that the genre was dying. Instead, short fiction thrived, copious and flexible. By the first decade of the twenty-first century approximately fifty collections were appearing every year, the increase in numbers coinciding with social growth, changes in the means of production, and extended critical attention. While markets for the genre remained fragile, short fiction nevertheless became more visible and more varied, with publishers seeking further ways to attract commercial attention and new writers keen to address readers in a different manner and voice. New stories ranged widely in subject and form: from Alice Munro’s Altmanesque collections, where western Ontario radiates into story-making, to Rohinton Mistry’s layered sequence set in Toronto and Bombay; from Alistair MacLeod’s memorializing Cape Breton cadences to the serious comedy of Austin Clarke and Thomas King; from Mavis Gallant’s and Guy Vanderhaeghe’s dances with temporality to the rhetorical adventures of Audrey Thomas, Tamas Dobozy, Mark Anthony Jarman, Douglas Glover, Thomas Wharton, and Lisa Moore. Numerous writers, such as Nancy Lee, Rachel Wyatt, Jack Hodgins, and Bill Gaston, dramatized the particularity of gender, place, and social politics. Some, such as Olive Senior, Adam Lewis Schroeder, and Joseph Boyden, focused on issues relating to ethnic and colonial history. Still others – among them Elizabeth Brewster, Kristjana Gunnars, Timothy Findley, Barry Dempster, Ken Mitchell, Mark Frutkin, and David Watmough – established their primary reputation with their poems, plays, or novels, but also published noteworthy collections of stories. The history of the genre during these decades thus demonstrates a series of differing designs, due in part to radical changes in technology, strategic developments in the publishing industry, and the emergence of succeeding generations of writers, all coping with the demands of the medium and the institutions of communication, and eager to express their understanding of their own time in the world.
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 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.007 | 0.003 |
| Scholarly communication | 0.009 | 0.006 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.243 | 0.110 |
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".