Reviewing First Nation land management regimes in Canada and exploring their relationship to community well-being
Bibliographic record
Abstract
The presented paper synthesizes and reviews the history of Fist Nation land management, forming the background of three land management regimes types; the Indian Act land management (IALM), First Nations land management (FNLM) and frameworks of self-government land management (SGLM). The three regimes are compared to the Community Well-Being (CWB) index, being a measure of socio-economic development of communities across Canada. Statistical analysis was done on CWB scores by land management regime to determine if there are significant differences between land management regime and CWB scores, and where rates of increase in CWB are found. Results of these efforts identified five key findings; 1) while higher levels of CWB score are found in all three land-management regimes, there is an increasing trajectory in CWB average scores from IALM, to FNLM, to SGLM communities; 2) there is a significant statistical difference between CWB average scores of the IALM with FNLM and SGLM land management regimes, 3) higher levels of CWB scores were found among communities having a formal versus an informal land tenure system; 4) rates of increase in CWB scores were found in higher scoring communities, however, the rates were higher at the lower quartile; 5) increase in CWB scores was observed in FNLM communities both prior and after transition to FNLM, however, the rate of increase slowed down after transition.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.007 | 0.024 |
| Science and technology studies | 0.006 | 0.002 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".