Reciprocal accountability and fiduciary duty: Implications for indigenous health in Canada, New Zealand and Australia
Bibliographic record
Abstract
There is growing interest among public servants, Indigenous organisations, and scholars in Canada, Australia, and New Zealand in the idea of shifting from classical New Public Management accountability models to models that reflect mutual or reciprocal accountability as a means of delivering more effective and responsive health care to Indigenous communities. However, little progress has been made with respect to developing and implementing workable reciprocal accountability models. In this paper, we argue that a consideration of Indigenous perspectives on reciprocity and accountability is an essential, yet mainly overlooked, component of the development of effective and appropriate accountability models between Indigenous peoples and statebased funders. Indeed, many Indigenous peoples have long histories of engaging in reciprocity-based relationships with each other and their environments. Drawing from Indigenous knowledge in this regard offers novel insights that can inform how models of reciprocity are constructed and understood. More specifically, we argue that consideration of Indigenous perspectives on treaties and treaty-making as a way to interpret the substance of mutual roles and responsibilities enables a shift to models of reciprocal accountability that are based on the mutual building of long-term, trust-based relationships, while also providing a frame that emphasises the maintenance of the sovereignty of the entities that are party to such relationships.
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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 | 0.020 | 0.039 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.036 | 0.054 |
| Scholarly communication | 0.012 | 0.008 |
| Open science | 0.003 | 0.015 |
| Research integrity | 0.005 | 0.008 |
| Insufficient payload (model declined to judge) | 0.005 | 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".