A Deal with the Devil: Practicalities vs Academic Accommodations in Teaching First Year Poetry Writing at Malaspina University-College
Bibliographic record
Abstract
I have lost count of the number of first year CW poetry students who have come up to me at about mid-semester and announced with innocent sincerity: “I didn’t know it was this hard to write a good poem!” I do know that the realisation of the effort in art and craft involved in poetry grows steadily with students in each succeeding year of CW workshops. I also do not know if a future Nobel Prize poet will emerge from a lonely garret somewhere beyond any CW class, or whether they will have taken a degree in CW at a University where a “deal” has been struck with the academic world. But despite all the academic accommodations involved in teaching CW poetry writing, I do know that many, many students who have taken CW classes will certainly appreciate the books of poetry which are published, attend poetry readings, and have a life-long appreciation of the tremendous demands of art and craft involved in writing a fine poem.
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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 | 0.009 | 0.029 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.013 | 0.011 |
| Scholarly communication | 0.011 | 0.005 |
| Open science | 0.002 | 0.011 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.013 | 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".