Geospatial Architecture for The Cree/Naskapi Land Registry System as An Economic Development Mechanism for Canadian Aboriginal People
Bibliographic record
Abstract
SUMMARY The aboriginal land registry system across Canada varies depending on diverse land rights regime for each First Nation group, such as their vision of the land, ownership and the environment, communal or ancestral land rights. Independently from land right regime, the land registry becomes an important and essential tool for economical development when a geospatial reference is attach to the land properties. Specifically for the Cree and Naskapi people in Northern Quebec, the adoption of a computerize information system allows them to manage their territory and the environment using the land registry as a first source. In addition, this information is essential to support an e-government and e-governance. This paper describes the results of a study for modernizing the analog Cree/Naskapi Land Registry by integrating it into the Indian Land Registry System (ILRS), which was developed by the Ministry of Indians and Northern Affairs Canada (INAC) for the First Nations. This work explains the Cree/Naskapi land rights regime, the conceptual land tenure differences between private land ownership, the legal cadastral issues, the geospatial infrastructure, the importance of land management in the Web, and the benefits in the economic development and land planning to the native communities in Northern Quebec.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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".