The Australian professional standards for educators: analysing cognitive, affective and pedagogical expectations in First Nations education policy
Bibliographic record
Abstract
Abstract The Australian Professional Standards for Teachers, Middle Leaders, and Principals detail the minimum professional expectations that educators ought to demonstrate at each level. They were developed to ensure equitable practices across the entire Australian education system, including schools and initial teacher education programs. However, limited scholarship has explored the capacity to which these professional standards assist educators in supporting nationally stated goals of First Nations students’ attendance, achievement and attainment. In this manuscript, we examine how the standards came to reflect and direct Australia’s sociopolitical and education policies and practices. We then undertake two connected analyses: one using taxonomies to critically assess the professional cognitive, affective, and pedagogical expectations of educators, and one using a poststructuralist lens to discuss the potential impacts of the standards on enacting equity and reconciliation in schooling. Our analysis reveals that in relation to the specifically tagged Aboriginal and/or Torres Strait Islander standards, the minimum expectations for all levels of professional practice are cognitively and affectively low but pedagogically high. We argue that these minimum expectations are complicit in perpetuating deficit cultural discourses of underachievement, and thus, call for a rethink of the actioning language of the standards to tackle ingrained sociopolitical structures of racism, relationality, and bias inherent in Australian schooling.
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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 | 0.015 | 0.031 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.005 | 0.009 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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".