Bibliographic record
Abstract
There was no way you could starve in this country', Saanich elder Dave Elliot Sr reminisced about his peoples' historic hunting and gathering territories in the 1980s.We had so much of everything.It would be impossible to starve.There was so much food, it was everywhere.This is why I say our people were so rich, not to mention the great salmon runs, the deer, the elk and so on.(Poth 1990: 48) Old Pierre of the Katzie group agreed.'In earlier times this Fraser River resembled an enormous dish that stored up food for all mankind', he related to an anthropologist in the 1930s (Suttles 1955: 10).The natural abundance of the Salish Sea, its islands and its mainland tributaries have been crucial to how successive human inhabitants have survived in and valued this region.Native peoples used and actively managed this abundance for thousands of years (see Angelbeck, this volume) and this same diversity of resources drew European, Euro-American/Canadian and other newcomers to the area from the late 1700s onward.The region produced raw materials for distant markets for decades, even as incoming white settlers increasingly displaced Indigenous peoples from their traditional lands and resource procurement sites.Coast Salish communities adapted as best they could but were largely shunted onto reservations (US)
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".