Sketching Possibilities: Poetry and Politically-engaged Academic Practice
Bibliographic record
Abstract
In this article I draw together and reflect upon my own experiences of writing poetry as a part of a politically-engaged academic life. My aim is to trace the political possibilities I have found in poetic practices, with the hope that describing and reflecting on my own experiences may illuminate pathways for others to integrate poetry into their academic practice. As I will detail, I have published research poetry and have been a leader of workshops that encourage academics to incorporate poetic and other forms evocative writing into their researcher toolkits. Often participants in these workshops have remarked how unusual it seems to think of poetry as a resource for academic work. I hope that this article might demonstrate some previously unimagined possibilities for new poetic enquirers, and provide stimulus for further thought for experienced practitioners to connect poetry and academic practice.
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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 | 0.019 | 0.035 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.019 | 0.101 |
| Scholarly communication | 0.025 | 0.022 |
| Open science | 0.002 | 0.014 |
| Research integrity | 0.004 | 0.010 |
| Insufficient payload (model declined to judge) | 0.007 | 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".