Bibliographic record
Abstract
Late teen, early 1980s, and first gorging on poetry because it was the only thing that made sense, I became deeply enthused by hearing poems, as much as by reading them: to experience poetry as immanent, in synaesthetic plenty, all writing, reading, listening, speaking, watching, touching, tasting and smelling of poetry was involved. Poets whose work was about all the things language could do, and all the ways of doing it, really appealed to me. Among Canadian poets, I was not left wanting. Amazing work had already been achieved in the experimental spirit of the sixties and seventies: sound, visual, concrete and performance poetry; experimental theatre; choral and improvised forms; dub; and small press activism had all contributed to a robust literary environment. Best of all, we had great mentors and lots of critical work published to base our work on, to push the envelope toward an ever-more imaginative and unbridled poetic passion. One could follow the work of bpNichol, Steve McCaffery, Lillian Allen, bill bissett, Roy Kiyooka and so many more, set free to consider far-reaching visions of poetry’s future. It was this openness to experimentation, this breadth of imagination, that made Canadian poetry a wonderful ride that cost very little money and took you a long way.
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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.016 | 0.065 |
| Scholarly communication | 0.019 | 0.008 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.010 | 0.001 |
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".