Positioning Asian Australian Writing: A Study of Three Australian Literary Anthologies
Bibliographic record
Abstract

 
 
 This paper examines three anthologies of Australian literature: The Oxford Anthology of Australian Literature (1985), The Macmillan Anthology of Australian Literature (1990), and Macquarie PEN The Anthology of Australian Literature (2010). It focusses on the politics of inclusion in anthology-making and the mechanics surrounding theme, perception, and ideology about literature as a reflection of a nation’s culture and identity. The increasing popularity of Asian Australian writing in recent years has raised concerns about how it is perceived as part of the national literary tradition that is a relatively white space. Not much is discussed about how Asian Australian writing appears in anthologies despite increasing critical attention given to them as a literary genre. Informed by Asian Australian studies and anthology criticism, this study charts the trends in the inclusion of Asian Australian writing and how it is posited within the larger national canon.
 
 
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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".