Bibliographic record
Abstract
This chapter examines the disconnect between Canada’s federal government ostensibly making national university research grants available to Creative Writing (CW) profs but only actually awarding one grant per year to Canada’s dozens of eligible CW profs. Since 2003, the Social Sciences and Humanities Research Council of Canada has invited CW and other artist-profs to apply for “research-creation” [R-C] grants from the billion public dollars they award each year, yet almost no Canadian writer-profs win grants for their creative research. This author is one of the very few Canadian CW profs who has won more than one faculty R-C grant and has also served as a SSHRC juror for both fellow faculty and doctoral candidates. His explanations for this funding disconnect include the fact that faculty artists and scholars are grouped into the same sub-competitions but with a majority of scholars on the juries, as well as the deemed irrelevance of project writing samples. Aesthetically, this chapter also explores the influence that pre-plotting large writing projects for public grant applications, including in proposing expenditures like research travel, may have on faculty art-making.
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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 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.022 | 0.015 |
| Scholarly communication | 0.015 | 0.014 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.005 | 0.008 |
| Insufficient payload (model declined to judge) | 0.024 | 0.006 |
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".