Futures for Australian curriculum studies: metaphors, genres and complicated conversations
Bibliographic record
Abstract
My specific brief for the conference presentation on which this essay is based was to speak from the standpoint of a 'curriculum theorist'. However, I rarely use the terms 'curriculum theory' or 'curriculum theorising' other than in the company of US and Canadian colleagues. I prefer to speak of 'curriculum inquiry' or 'curriculum work' and I think of my work as a university teacher and researcher as being directed towards understanding curriculum. From this standpoint I interpret the theme of this Point and Counterpoint, 'Futures for Australian Curriculum', as a focus for speculation on the possible and desirable ways in which the arts of curriculum inquiry can be developed, tested and renewed. In other words, how can we sustain rigorous, vigorous and generative forms of curriculum work? I will respond to this question by referring to three artefacts of Australian curriculum studies, the first two of which come from the Australian Curriculum Studies Association's (ACSA) own material history; the third is (arguably) the major synoptic text of North American 'curriculum theory' published during the past decade. I will use these artefacts to illustrate three key issues concerning futures in curriculum inquiry, namely: • the significance of metaphor; • questions about genre and a renewed role for the arts in our work; • the idea of 'complicated conversation'.
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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 | 0.021 | 0.028 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.024 | 0.047 |
| Scholarly communication | 0.018 | 0.014 |
| Open science | 0.002 | 0.013 |
| Research integrity | 0.004 | 0.009 |
| Insufficient payload (model declined to judge) | 0.005 | 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".