An Investigation of Coordinates as Mathematical Evidence for Cadastral Surveying in Alberta
Bibliographic record
Abstract
This research delves into the topic of coordinates as legal survey evidence for boundary positions. It is spurred by the recently adopted Hybrid Cadastre project in Alberta, Canada and the evidentiary changes concurrent with this initiative. A review of literature pertaining to how survey evidence in assessed historically under the Hierarchy of Evidence, as well as the implications of modern evidentiary initiatives with coordinates is provided. Hypotheses are formed from this literature review and enlighten the qualitative study design. Key informant interviews apprise of the profession’s perspectives on this form of evidence are assessed using qualitative methods. Informants included practicing land surveyors, academics, members of the public, and government officials. A descriptive narrative approach was applied to the informant’s feedback to generate emergent themes. Informant feedback was assessed against the themes by incorporating an ordinal scale to provide a parameterized data set. Inferences made from this dataset prime the synthesis and theory development. In synthesis, an emergent theory on coordinates as evidence is provided as well as a continuum for assessing coordinate based evidentiary initiatives. When properly framed within the legislative framework and in specific de facto applications coordinates can govern legal survey boundaries and be considered a sui generis form of boundary evidence. An emergent continuum is proposed to provide a metric for assessing future applications of coordinates as evidence in alternate jurisdictions. This continuum is founded in the principles of cadastral management, and ensuring the public's continued faith in the land framework. Conclusions are provided relating to the adoption of coordinates as evidence currently within the land framework and case law. Ultimately future adoption of coordinates as evidence is a topic that requires legislative intervention to provide for widespread adoption and acceptance.
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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 | 0.019 | 0.037 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.005 | 0.010 |
| Science and technology studies | 0.011 | 0.012 |
| Scholarly communication | 0.010 | 0.003 |
| Open science | 0.003 | 0.007 |
| 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".