Poetry as Investigative Pedagogy: Issues of Ethics and Praxis in Hay and Thorne’s Last Days of the Mill, 2012.
Bibliographic record
Abstract
This paper examines dilemmas of ethics and practice in the author’s co-written Last Days of the Mill (2012). The usefulness of poetry as a tool of social inquiry is considered, both in the immediate context of a dying pulp mill in an industrial town in northern Tasmania, and the wider symbolic import of the mill’s demise within an island wedded to an unrealisable vision of industrial greatness. It is argued that there are forms of knowing in which poetry is far more efficacious than analytical prose, most notably elusive and grounded understandings such as ‘being there-ness’, and the accretion of a vividly storied mindscape expressed through the spoken word. The paper then considers the injunction of the Canadian poet, Robert Bringhurst – that ‘when he sees his people destroying the world, the poet can say, “we’re destroying the world”. He can say it in narrative or lyric or dramatic or meditative form, tragic or ironic form, short or long form . . . But he cannot lie, as a poet . . . ’ The paper argues for a more nuanced and inclusivist ethic, even when, technically speaking, this requires an act of dissimulation on the part of the poet.
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 | 0.007 | 0.011 |
| 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 | 0.063 |
| Scholarly communication | 0.012 | 0.010 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.005 | 0.008 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".