Policy fuzz and fuzzy logic: researching contemporary Indigenous education and parent–school engagement in north Australia
Bibliographic record
Abstract
‘Engagement’ is the second of six top priorities in Australia's most recent Indigenous education strategy to ‘close the gap’ in schooling outcomes. Drawing on findings from a three‐year ethnographic analysis of school engagement issues in the north of Australia, this article situates engagement within the history of Indigenous education policy, followed by considerations of how many of the issues faced by Indigenous families both match and can be distinguished from those experienced among poor and underemployed social groups throughout the western world. We find that Indigenous people are content with the schools' engagement efforts and with their interactions with schools, accepting that how their lives are lived are not within the provenance of the school system to amend. In its homogenisation of Indigenous issues, reification of cultural distinction and foregrounding of disengagement as an issue, Australian education policy is also about non‐engagement, in that it excludes key issues from policy consideration while appearing to be inclusive. The education sector does not systematically engage with the grinding issues that Indigenous families face in their everyday worlds; and since Indigenous people do not really expect schools to know how to solve their issues, the call for engagement and its resolution is perfectly irresolvable.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.007 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.010 | 0.016 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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".