Institutional Change On First Nations: Examining Factors Influencing First Nations’ Adoption of the Framework Agreement on First Nation Land Management
Bibliographic record
Abstract
In 1999 the Canadian Federal government passed the First Nations Land Management Act, ratifying the Framework Agreement on First Nation Land Management signed by the government and 14 original signatory First Nations in 1996. This Agreement allows First Nations to opt out of the 34 land code provisions of the Indian Act and develop individual land codes, and has been promoted as a means of increasing First Nation autonomy and facilitating economic growth and development on reserve lands. There are currently 77 First Nation signatories to the Agreement, 39 with operational independent land codes. This paper is the first to empirically examine factors that may influence a First Nation's decision to become signatory to the Framework Agreement. A unique dataset characterizing each First Nation by socio-economic and demographic characteristics is used with a probit model to determine the effects of these characteristics on the probability of First Nation adoption of the Agreement. The results of this study indicate that proximity to an urban centre positively affects the probability that a First Nation will adopt. However, the statistical strength of this finding is sensitive to the inclusion of an education variable in the regression.
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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 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".