Bibliographic record
Abstract
This chapter studies the $100,000 Scotiabank Giller Prize for Fiction, the most prestigious of Canada’s national literary prizes, and the impact of James English’s “economy of prestige” on Canada’s writing professors and students. Canada’s career-making prizes claim in various ways that their selections contribute to a corpus of books that succeed in representing the diverse ethnic, regional, linguistic and literary culture/s of a vast and diverse nation. This chapter will show that the vaunted representativeness of major Canadian literary prizes is more wishful thinking than real. Prestige-building through efforts to secure international recognition is a feature of the intensifying competition amongst Canada’s numerous literary prizes. The upshot is that the manoeuvering to achieve global reach and impact belie prizes’ declared commitment to a national literature, including one that begins, in part, in postgraduate writing programs. There are yet other contradictions: the national has come to be what is constructed (imagined) for the sake of the popular as well as for appeal to readers abroad. Paradoxically, the enhancement of national prize-related prestige increasingly entails the exchange of local scales of value for international ones.
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 | 0.004 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.009 | 0.013 |
| Scholarly communication | 0.021 | 0.009 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.015 | 0.003 |
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".