Applications of Ecological Footprint and Biocapacity to Saugeen Ojibway Nation Land Claims
Bibliographic record
Abstract
This research project is a case study on the Saugeen Ojibway Nation (SON) land claims in Ontario and relates to themes of Indigenous Truth and Occupation. It applies metrics of Ecological Footprint and Biocapacity to assess the value of land dispossessed through colonialism. Traditional SON lands in Ontario are currently owned by provincial and federal governments since they were stolen by the British Crown upon the breaching of Treaty 45.5 in 1854. This treaty ceded 1.5 million acres of land to the British Crown in exchange for their promise to protect the Saugeen Peninsula forever. SON states that the Crown misled them in negotiations regarding the surrendering of their land, thus dispossessing them of their traditional territory. Through this case, they are seeking ownership of land not owned by third parties, recognition of title, and financial compensation. \n\nEcological Footprint and Biocapacity metrics can be used to assess the value of the land associated with the claim. To do so, the biocapacity of the land is calculated and multiplied by the monetary value of land per hectare in Ontario to assess the monetary value of what was dispossessed. Assessing the monetary value of the land that was dispossessed speaks to political interests, is easily recognizable by a large audience, and can be applied to phase 2 of the case where financial compensation will be determined. Research on the exclusive and sufficient use of SON’s traditional territory prior to the breaching of treaty will also be applied to this case. \n\nIt is important to consider the biocapacity of a region when making legal decisions regarding land claim cases. Collecting data on biocapacity and the use of the region being considered is important in determining any financial compensation that the community may or may not receive. Ecological Footprint and Biocapacity metrics show the loss of physical land and resources, and the ways that environmental research can influence legal decisions.
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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 | 0.003 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.005 | 0.008 |
| Science and technology studies | 0.003 | 0.006 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| 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".