Enacting Kaitiakitanga: Challenges and Complexities in the Governance and Ownership of Rongoā Research Information
Bibliographic record
Abstract
This article explores the tensions one research team has faced in securing appropriate governance or stewardship (which we refer to as kaitiakitanga) of research data. Whilst ethical and regulatory frameworks exist which provide a minimum standard for researchers to meet when working with Māori, what our experience has highlighted is there is currently a “governance” gap in terms of who should hold stewardship of research data collected from Māori individuals or collectives. In the case of a project undertaken in the traditional healing space, the organisation best placed to fulfil this governance role receives no funding or support to take on such a responsibility; consequently by default, this role is being borne by the research team until such time as capacity can be built and adequate resourcing secured. In addition, we have realised that the tensions played out in this research project have implications for the broader issue of how we protect traditional knowledge in a modern intellectual property law context, and once again how we adequately support those, often community-based organisations, who work at the interface between Indigenous knowledge and the Western world.
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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 | 0.080 | 0.068 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.012 | 0.034 |
| Scholarly communication | 0.024 | 0.017 |
| Open science | 0.003 | 0.016 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.002 | 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".