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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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 teacher head, 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".