Reconciling blue spaces: evaluating social equity and justice in state-led Indigenous marine development programs
Bibliographic record
Abstract
Context Government-led Indigenous marine development programs aim to deliver socially equitable outcomes, yet these principles are not always embedded in their design. Aims Evaluating the extent to which equity is prioritised is crucial for respecting Indigenous rights, interests and advancing reconciliation. Methods This study introduces the Blue Peacebuilding Scorecard (BPS), a novel quantitative tool to assess social equity integration in marine-based Indigenous programs across Australia, Canada and New Zealand. Key results New Zealand emerged as a leader in equity and justice, Canadian programs displayed varied performance and Australian programs ranked moderately overall. Justice was a consistently strong category across all nine programs, indicating explicit consideration in program design internationally. Areas such as legacy, access and finance showed moderate performance globally, highlighting opportunities for improvement. Conclusions To enhance social equity and advance Indigenous rights within national blue economy development, emphasis should be placed on impact assessment, data sharing and recognising data ownership as critical pathways for progress. Implications The BPS can be adapted to provide localised insights, fostering reconciliation and collaboration. By facilitating co-learning and reducing both stakeholder conflict and fatigue, the use of this practical tool can lead to enhanced program effectiveness and improved relationships.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.008 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.006 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.006 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".