Nature's Past Episode 002: Natural Resource Development in British Columbia
Bibliographic record
Abstract
This month’s episode focuses on resource development in British Columbia. Last November, the Nature/History/Society group hosted a roundtable on hydro in BC, featuring Jeremy Mouat (University of Alberta), Tina Loo (University of British Columbia), and Paul Hirt (Arizona State). In this episode we highlight a selection from Tina Loo’s talk on hydro-electric development and high modernism called ‘Towards an Environmental History of ‘Progress’. You can listen to the full roundtable on hydro in BC here. \n \nAlso, this month we feature an interview with Jonathan Peyton, a Ph.D. candidate in the Department of Geography at UBC who is studying the history of resource conflict in the Stikine Plateau region of northern British Columbia. \n \nGuests: \nJeremy Mouat \n \nTina Loo \n \nPaul Hirt \n \nJonathan Peyton \n \nWork Cited: \nOn the Environment” Special Issue BC Studies 142/143 (Summer/Autumn 2004) \n \nMusic Credits: \n“See You Later“ by Pixt \n \n“Smoke” by Pixt \n \n“No” by Pixt \n \n“Fun Key” by Pixt
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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 | 0.006 |
| Science and technology studies | 0.023 | 0.002 |
| Scholarly communication | 0.007 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.037 | 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".