Lyell Island (Athlii Gwaii) Case Study: Social Innovation by the Haida Nation
Bibliographic record
Abstract
The logging blockade on Lyell Island in British Columbia, Canada in 1985, together with the events surrounding it, was an important indigenous-led social innovation by the Haida Nation. The social innovation itself was three-fold: (1) it changed the way indigenous nations in Canada reasserted themselves as self-determining; (2) for the Haida Nation to assert their Aboriginal rights and title to the land and resources of Haida Gwaii was an important step, the first of many; and (3) it changed the way environmental campaigns were conducted, both in Canada and internationally. In the 1980s relations between indigenous nations and the British Columbian and Canadian governments were embedded in an enduring, patriarchal-colonial sociopolitical and legal context. The Haida Nation's assertion of land rights and title was an initiative that changed the basic routines, authority flows and beliefs of the social system in British Columbia and Canada. The message that the Haida Nation's traditional territory was not to be exploited in a way that was incongruent with their visions of stewardship of their land had broad and lasting impact that clearly changed a larger institutional and sociopolitical context. The Haida not only created a precedent, but also a catalyst for action with regards to co-management, environmental advocacy, indigenous governance and Aboriginal rights.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.028 | 0.008 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".