Can We Talk About How, Not Just What? Teaching Australian, Indigenous, and World Literatures in Complex Classrooms
Bibliographic record
Abstract
In his essay collection Of Color, Jaswinder Bolina observes: The white masters, masters though they may be, are oblivious to those experiences of bigotry and exclusion that are condemnably common for the rest of us. In this essential matter, those writers of the literary canon are utterly ignorant, and so their reports on the human condition are gapingly incomplete. (302) While the practice of teaching literature that challenges dominant worldviews may present students with expanded narratives of the human condition, the often-unacknowledged effects of colonialism in contemporary classrooms can make literary studies a conflict zone. Diana Brydon acknowledges this in her essay, “Cross-Talk, Postcolonial Pedagogy, and Transnational Literacy.” Yet, apart from Brydon, only a handful of scholars, including Ingrid Johnston, Jyoti Mangat, and Cynthia Sugars in Canada, or Sandra Phillips and Clare Archer-Lean in Australia, have raised the issue of how difficult texts might be unpacked in complex classrooms. In general, most literary studies academics continue to focus their scholarship on the world of the text, instead of the dynamics of the text in the world. Given the growing interest in and teaching of Indigenous lives and texts, as well as a resurgence of interest in the legacies of colonialism raised by movements like Black Lives Matter, Free Palestine, and the Australian referendum on the Voice to Parliament, there is more to unpack in the classroom encounter.
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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 | 0.009 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.020 | 0.039 |
| Scholarly communication | 0.014 | 0.018 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.004 | 0.009 |
| Insufficient payload (model declined to judge) | 0.004 | 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".