Development of an agent-based First Nation land use voting model: Experiments in policy adoption at Curve Lake First Nation, Canada
Why this work is in the frame
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Bibliographic record
Abstract
Land use plans and policies provide a pathway for communities to achieve a vision for future types and arrangements of land uses as well as to formalize the objectives needed to realize that vision. Members of a community often share a common vision, but differ on how it can be achieved, which is the case at Curve Lake First Nation. To investigate the factors affecting land-use plan and policy adoption at Curve Lake First Nation, a stylized agent-based model, the First Nation Land Use Voting Model (FNLUVM), was developed in collaboration with Curve Lake First Nation and was empirically informed from a survey of its members (n = 156). A series of experiments were conducted with FN-LUVM to understand the effects of land knowledge, attitudes, and community engagement among both non-land holders and land holders in certificate of possession on adopting a land use plan and policy adoption. Among several findings, results of these experiments suggest 1) that members with shared land-stewardship and ambition for improvements in socio-economic well-being were key proponents for adoption, 2) community engagement with members typically unwilling to collaborate with others can reduce disconnect among members, 3) improving knowledge about land planning and policy among members can lead to more engagement in voting and support for land use plans and policies. While the collaborative development of FNLUVM was specific to Curve Lake First Nation, it is made available for other communities to customize and use as a medium for discussion or decision-making support tool. • A novel agent-based voting model, developed with a First Nation, is presented. • Key influencers have the ability to affect support for land use plans and policies. • Members without land holdings gained most from land knowledge acquisition. • Members with balanced perspectives were more likely to support plans and policies.
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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 | 0.000 | 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 | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 it