Aboriginal oral testimony, hearsay rule and the reception theory of admissibility
Bibliographic record
Abstract
Aboriginal peoples title claims are presumed upon spatial and time connections to the lands of their ancestors. In making their submissions, litigants have to circumvent the rule against hearsay and rely upon oral narratives to substantiate their claims of customary ties to land. The obstacles they face is that evidence based on informal anecdotes can cause problems in common law courts, which have long been dependent on textual evidence for probative value. In many Native cultures the idea of time is cyclical, while in the Judeo-Christian calendar time is linear. There is also the fact that oral narratives cannot be viewed in the abstract and the histories are closely linked to inter-generational continuity. The perspective of a narrator is relevant as the sources are often repositories of observation, knowledge and personal belief rather than clear factual understanding of the issue involved. This paper argues for the receptive theory of oral evidence to be adopted in common law courts, which would lead to a fair hearing of Aboriginal claims to land title in Australian and Canadian courts. The paper will distinguish the courts’ current approach to oral testimony submitted by aboriginal people and raise the possibility of an integrated approach based on the recourse to ‘episteme’, which is the appreciation derived from synthesis that accepts that several methodologies may exist and interact at the same time by being parts of various knowledge systems.
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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 | 0.016 | 0.043 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.005 | 0.028 |
| Scholarly communication | 0.010 | 0.007 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".