Indigenous knowledge and effective parent–school partnerships : issues and insights
Bibliographic record
Abstract
Towards the last decade of the last millennium, Indigenous knowledge was central to international scholarly debates relating to decolonising knowledge. Indigenous scholars, particularly those from Australia, New Zealand, Canada, and the United States, advanced many of these debates. They argued for Indigenous knowledge to be used as the epistemological standpoint for intellectual engagements and the methodology for resisting colonial constructions of the colonised other (Rigney 1997; Smith 1999, 2005). However, the challenge of engaging Indigenous knowledge to inform research and educational processes, in many respects, is still a contested debate in Western-oriented universities and institutions of higher education. This chapter discusses findings of the Parent–School Partnership Initiative (hereafter referred to as PSPI) project conducted by the Oodgeroo Unit staff and the Aboriginal and Torres Strait Islander Education Focus Group for the Caboolture Shire, in South East Queensland. The state government sponsored initiative examined factors that promote and enhance parent–school engagement with students’ schooling, and contributed to Aboriginal and Torres Strait Islander students’ learning and completion of secondary schooling within the participating schools. We argue in this chapter for the importance of recognising Indigenous knowledge and its place in enhancing parent–school partnerships.
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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 | 0.003 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.006 | 0.010 |
| Scholarly communication | 0.008 | 0.009 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".