Development of an agent-based First Nation land use voting model: Experiments in policy adoption at Curve Lake First Nation, Canada
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.
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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 | 0.003 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".