A co-created model for self-determined development objectives in Indigenous communities
Bibliographic record
Abstract
The Eurocentric approach to development has several implications for the Indigenous communities and their way of life, including the decline of the social and cultural values of the people as well as threats to their identity. The exclusion of people from development processes has led to contextual misalignment of development and local value systems. Inclusion and participation in the development processes will allow for self-determined development and contextual alignment of the outcomes communities. Self-determined development will allow Indigenous communities to control their own fate and possibly preserve their way of life and cultural identity while empowering them to carve solutions grounded in their worldviews to modern problems. One way to allow self-determined development is to empower Indigenous communities to chart their development goals. However, the central challenge is how to do this. To find a solution, we examine the question, “What are the processes necessary for creating endogenous development objectives in Indigenous communities?” In this study, an inclusive and qualitative approach is taken to co-create a model for self-determined development in Indigenous communities in the Rupununi. This model for self-determined development can be adopted to empower the people to have a voice in development policies and programs in the region.
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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 | 0.006 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.005 | 0.008 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".