Standards dor Oral Tradition Evidence: Guidelines From First Nations Land Claims in Canada
Bibliographic record
Abstract
Conventional land registration systems have served to underpin particular forms of land tenure since ancient Babylon and perhaps before that. However, there are a number of tenure forms which are ill served by the systems that have evolved from these early systems. For example, 1 billion people live in slums in urban areas where tenure systems often draw on both customary and western tenure practices and some 300 million First Peoples live in different countries around the world. Insecure tenure is a major issue for these communities and often result in conflicts and tensions when they try and defend their rights. Nowadays, we have the technology to capture oral tradition and oral history evidence. However, the courts in Canada have struggled to handle evidence which draws on stories that incorporate myth, legend and fact. The common law itself has had to evolve in order to adjudicate Aboriginal land claims fairly and so recognize the unique, sui generis, nature of these rights. A number of Canadian cases in the last twenty years have also developed guiding principles for how oral tradition and oral history evidence should be presented and examined. This in turn provides guidance on how this type of data should be stored and documented. The challenge is to include this data in a land information system in a manner which will be acceptable to the courts.
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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 | 0.230 | 0.396 |
| Meta-epidemiology (narrow) | 0.002 | 0.005 |
| Meta-epidemiology (broad) | 0.004 | 0.005 |
| Bibliometrics | 0.026 | 0.021 |
| Science and technology studies | 0.024 | 0.023 |
| Scholarly communication | 0.031 | 0.010 |
| Open science | 0.024 | 0.018 |
| Research integrity | 0.029 | 0.018 |
| Insufficient payload (model declined to judge) | 0.005 | 0.003 |
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".