Indigenous Takes On Environmentalism: An Interview on the Front Lines of Indigenous Land Defense
Bibliographic record
Abstract
indigenous takes on Environmentalism: An interview on the Front lines of indigenous land defense Megan Kinch 1 in conversation with Giibwanisi, Anishinaabek Nation 2 ; Kaikakons, Anishinaabek Naiton 3 ; Sleeping Grizzly, Haudenosaunee Nation 4 baCkground and ContextDoing an interview with someone who is currently doing a land reclamation meant that the usual email method had to be modified a bit.As Giibwanasi is currently on location at the site, answering my emailed questions would have meant typing out essays on a smartphone.So instead, after a long day chopping wood and doing work, he read my questions to himself and his friends Kaikaikons and Sleeping Grizzly, and they answered in the form of a conversation by the fire.The Oshkimaadiziig Unity Camp is a land occupation and cultural revitalization project: the Anishinabek Confederacy To Invoke Our Nationhood (ACTION).What follows, is a shortened and condensed version of their discussion. disCussionMegan kinch (Mk): Mainstream environmentalism has been hopelessly sidetracked into greenwashing, environmental capitalism and tokenistic gestures.When even David Suzuki is writing essays about the failure of environmentalism, we know that something has gone wrong.People
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.039 | 0.020 |
| Scholarly communication | 0.006 | 0.007 |
| Open science | 0.001 | 0.009 |
| Research integrity | 0.006 | 0.016 |
| 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".