"I Really Wanted to Write Novels That Are Poems": The multiple Effects of Poetry in Thea Astley's Fiction"
Bibliographic record
Abstract
In 1944, nineteen-year-old Thea Astley published “Poetic Fire” as her first contribution to Barjai, an arts magazine founded a year earlier by Brisbane State High students Laurence Collinson and Barrie Reid. Brave and witty but also defensive, Astley’s essay presupposes her identity as a poet and marks a stage in what was to be a continuing investigation of poets and the writing and reception of poetry. Astley began this investigation when she was a schoolgirl and continued it in the fiction she wrote between 1956 and 1999, i.e. throughout her career as a prose author. In 1986 she affirmed in an interview that she “really wanted to write novels that are poems” (Richey 99-100). This aspiration explains both the poetic personas and quotations from Astley’s poems that abound in her fiction and the “idiosyncratic, image-encrusted style” (O.C.A.L) that has sparked critical debate for decades.
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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 | 0.004 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.018 | 0.032 |
| Scholarly communication | 0.008 | 0.006 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.007 |
| Insufficient payload (model declined to judge) | 0.002 | 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".