Integrating First Nations peoples' cultural capital for sustainable development
Bibliographic record
Abstract
Abstract First Nations peoples occupy one‐quarter of the world's land area, safeguarding 80% of its biodiversity. Sustainable development frameworks acknowledge and include culture's role but fail to give it a special place, specifically First Nations peoples' (Indigenous) cultures. Hence, this study presents a sustainable development model that recognises their cultures—the underlying motivation is that adopting the United Nations Sustainable Development Goals (UN SDGs) as the 2030 Sustainability Agenda for these peoples' cultural capital development has posed two challenges. First, the goal‐related targets and indicators are objectified, encouraging these to be attained as separate goals, but since First Nations cultures are based on relationships and interconnectedness, thinking linearly about these goals misaligns with these cultures. Second, these targets and indicators are not framed to provide special recognition and inclusion of these peoples' cultural knowledge as crucial for sustainable development. Therefore, this study uses the Gaia theory, the theory of distributive justice and the interaction theory of First Nations cultures to propose an empirically testable structural equation model for analysing empirical data using the UN SDGs as goal posts, towards advancing sustainable development. A model application is proposed for non‐governmental organisations serving First Nations peoples. The integrated model shows the interrelationships between various types of capital, including these peoples' cultural capital, required for sustainable development.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".