ATR Process Geomatics: Process, Content, and Style
Bibliographic record
Abstract
This portfolio dissertation examines the Addition to Reserve (ATR) for new reserve land parcel recovery within the Indian Act and other land management regimes applying to First Nations in Canada. The three portfolio papers explore a land-based geomatics data perspective, exploring its process, data practice, and process history-engagement style. The ATR process geomatics is an emerging topic that has moved to a digital post-counter mapping practice, with land title transfer factors outlined in the portfolio. The findings illustrate the importance of understanding complex ATR land transfer geomatics topics for all First Nations, and showing that a two-eyed seeing data protocol is required in the updated reserve lands transfer program and practice. A. A policy brief on the ATR process (policy and geomatics practice critique)B. A practice primer on the ATR process (on geomatics practice and the ATR process data) C. Two-eyed seeing ATR process geomatics: enabling Canada's land back program, all ATR outcomes and geomatics practice needs (journal article on ATR history & practice style). The suggested study structure for the portfolio dissertation utilizes:• Synthesis essay (a guide to the portfolio dissertation) • Policy brief (bibliography specific to the paper) • Practice paper (bibliography specific to the paper) • Journal article (bibliography specific to the article) • Appendices (including specific bibliography to each appendix as needed)
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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 | 0.010 | 0.029 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.007 |
| Science and technology studies | 0.006 | 0.011 |
| Scholarly communication | 0.023 | 0.008 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.001 | 0.004 |
| Insufficient payload (model declined to judge) | 0.018 | 0.004 |
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".