Re-establishing innate connection to nature: an application of Indigenous ancestral practice in a new land-based learning centre on the French River
Bibliographic record
Abstract
The common society is coming to the realization that the earth can no longer sustain the human race if we continue to expropriate it in the manner and velocity that we currently are. Unethical mining, fracking, mono-forestry, and factory farming are all symptoms of a greater problem; the blatant disregard for the other living beings that accompany us on the planet. The question then becomes, how can we as human beings re-discover our innate connection to the natural world and therefore heal our relationship to the planet and ourselves? In response, we need to alter our relationship with ourselves and nature in order to prevent the continued exploitation of the world. We must go back to the ancestral knowledge carriers: the Indigenous peoples of the earth, in particular the Indigenous peoples of North America, since it is their land in which we now live. We all at one point in our history had a connection to The Land through intergenerational knowledge passed down in place through our ancestors. It is now our responsibility to re-establish that connection to the land if we are to have any hope in saving it. A return to the land is the answer to the fundamental change that we as human beings need to take in order to effectively take our place as the instruments of great good for the ecosystems around us. Educational environments that focus on living with the land need to be implemented at a greater rate and scale, and that is precisely what this thesis seeks to accomplish with the formation of a landbased learning centre. The centre will be placed along a remote corridor of the French River and serve as a junction between the old world and the new, accessible through modes of transportation such as canoe or car. It will sit on park land currently claimed by Parks Canada, juxtaposed with Dokis First Nation across the river, while also being on the boundary lines of three city districts. The programs will consist of the collaborative efforts of the local Indigenous peoples from Dokis First Nation, the Maamwazing Research Institute as the academic presence, and the Ministry of Natural Resources as the Provincial Governmental group. It will serve as a potential model for how local Indigenous groups, Academics, and Provincial Governments can co-operate and comanage together.
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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 | 0.007 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.033 | 0.016 |
| Scholarly communication | 0.009 | 0.006 |
| Open science | 0.003 | 0.010 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.014 | 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".