Can a formalised model of co-production contribute to empowering indigenous communities in decisions about land use?
Bibliographic record
Abstract
Purpose Co-production has been used in public services in the UK areas such as mental health to improve the participation of service users in decisions made about the services traditionally provided for them and done to them. It has also been used in areas such as mental health and to address concerns about the quality of services provided to members of minority communities. Western Australia is currently passing legislation to address the issue of aboriginal cultural heritage management in the context of recent adverse incidents such as the incident where Rio Tinto was responsible for the destruction of the site. This paper aims to show how a formalised model of co-production can assist in the implementation of this legislation. Design/methodology/approach This paper considers how effective co-production has been within the domain of mental health services in the UK and then considers whether they are lessons that may be learnt in other contexts. It considers whether concepts from co-production have a role to play in ensuring that the legislation and its implementation are not seen as actions done to or on behalf of the aboriginal communities and if a more structured approach to coproduction can produce a model, which facilitates genuinely collaborative aboriginal heritage management. Findings The approach has facilitated the development of a model to monitor and improve collaboration within aboriginal cultural heritage management, which complements existing participatory approaches and enables businesses to demonstrate their legislatory compliance. Social implications The study offers an approach, which may be used globally to empower indigenous communities in decision-making in other contexts, such as deforestation in South America and oil and gas exploitation on Inuit and First Nations land in Canada. Originality/value The use of co-production concepts and capability modelling is novel in this space.
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How this classification was reachedexpand
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Theoretical or conceptual | low |
| gpt | no category Domain: not available · Genre: Other About the Canadian research system: no · About a Canadian topic: no | Theoretical or conceptual | medium |
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 | 0.019 | 0.025 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.027 |
| Scholarly communication | 0.010 | 0.013 |
| Open science | 0.004 | 0.013 |
| Research integrity | 0.005 | 0.003 |
| Insufficient payload (model declined to judge) | 0.017 | 0.002 |
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, unvalidatedLabeled directly by 2 models reading the full record.
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".