Hishuk Tsawak (Everything is connected): a Huu-ay-aht worldview for seeing forestry in British Columbia, Canada
Bibliographic record
Abstract
While landscape may be read, understood, and imagined in pluralistic and contested terms, the power to define the landscape is typically held by a particular group of people. Government, industry, environmentalists, and First Nations, each representing distinct worldviews about the landscape, are among the key stakeholders in an ongoing struggle over the power to define the meaning and future use of BC's forests. The work reported here draws on interview data from a community-based participatory program of research undertaken in partnership with Huu-ay-aht First Nation on the west coast of Vancouver Island that explores the 'place' of their worldview in the context of current forestry practices. Specifically, this paper examines how the Huu-ay-aht worldview, Hishuk Tsawak, shapes their reading, understanding, and imagining of the forest landscape in their traditional territory. Hishuk Tsawak does not exist in a vacuum and dominant, competing worldviews from, for example, government and industry continue to test its resilience. The study found that Huu-ay-aht First Nation's physical and social locations are influential in determining its worldview's strength and continuity and concludes that Indigenous worldviews, such as Hishuk Tsawak, have the potential to contribute, contest, and conceive of a new way of seeing forestry in the province.
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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 | 0.040 | 0.009 |
| Scholarly communication | 0.007 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.004 |
| Insufficient payload (model declined to judge) | 0.005 | 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".