Nature's Past Episode 048: Ecotones and Saskatchewan History
Bibliographic record
Abstract
Arguably, the predominant landscape Canadians generally associate with Saskatchewan is one filled with waving grains of wheat and broad, flat vistas. It is the land of the living skies and one of Canada’s so-called Prairie provinces. And yet so much of Saskatchewan isn’t prairie. In fact, the prairie ecological zone covers only the southernmost part of the province. What about the rest? \n \nMerle Massie confronts this matter in her award-winning book, Forest Prairie Edge: Place History in Saskatchewan. It is a book that takes readers through a different landscape in the province of Saskatchewan and invites us to think about the province’s history from a new perspective: a view from the edge. That is to say, Massie shifts her focus in Saskatchewan history away from the predominant narratives about the prairies and agricultural settlement based on the cultivation of wheat toward the province’s ecotone, the transitional zone between the prairie and the parkland, the forest edge. \n \nIt is at the forest edge that Massie finds different ways of thinking about sustainability, European and Euro-Canadian colonization of the West, and other relationships between people and the rest of nature. This episode of the podcast features an interview with Merle Massie about her fascinating new book. \n \nPlease be sure to take a moment to review this podcast on our iTunes page.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.020 | 0.004 |
| Scholarly communication | 0.007 | 0.003 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.036 | 0.002 |
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".