Akimiski Island, Nunavut, Canada: The Use of Cree Oral History and Sea-Level Retrodiction to Resolve Aboriginal Title
Bibliographic record
Abstract
On 1 April 1999, Akimiski Island of the western James Bay region of northern Ontario, Canada, was included in the newly formed territory of Nunavut, Canada—an Inuit-dominated territory—even though the Inuit had never asserted Aboriginal title to the island. By contrast, the Omushkegowuk Cree of the western James Bay region have asserted Aboriginal title to Akimiski Island. The Government of Canada by their action (or inaction) has reversed the onus of responsibility for proof of Aboriginal title from the Inuit to the Cree. In other words, the Government of Canada did not follow their own guidelines and the common-law test for proof of Aboriginal title. In this paper, we documented and employed Cree oral history as well as a sea-level retrodiction (based on state-of-the-art numerical modeling of past sea-level changes in James Bay), which incorporated a modified ICE-6G ice history and a 3-D model of Earth structure, to establish that criterion 2 of the test for Aboriginal title has now been fully met. In other words, Cree traditional use and occupancy of Akimiski Island was considered sufficiently factual at the time of assertion of sovereignty by European nations. As all the criteria of the common-law test for proof of Aboriginal title in Canada, with respect to Akimiski Island, have now been addressed, the Cree have sufficient basis to initiate the process of a formal land claim.
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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 | 0.002 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.006 | 0.004 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".