Global Warming and the Sweetness of Life : A Tar Sands Tale | Matt Hern
Bibliographic record
Abstract
Lecture, October 18, 2018. 6:15 pm, Room 521, College Building. The Liberal Arts division and the department of History, Philosophy + the Social Sciences welcome writer/activist Matt Hern for a talk called Global Warming and the Sweetness of Life: A Tar Sands Tale. Hern is co-author of the recent book Global Warming and the Sweetness of Life (MIT, 2018), which charts multiple trips through the tar sands of northern Alberta and documents the effects of global warming on indigenous communities. Hern and co-creators Am Johal and Joe Sacco offer new forms of thinking about global warming and ecological perils in the context of class and de-colonial politics and seek new definitions of the word ecology. Matt Hern is a community organizer, independent scholar, writer and activist based in East Vancouver, British Columbia (Coast Salish Territories). He is known for his work in radical urbanism, community development, ecology and alternative forms of education. He is currently the co-founder and co-director of a creative production cooperative with and for refugees and recently arrived youth called Solid State Industries. Hern teaches at multiple universities, lectures globally and is widely-referenced in radical political and social discourses. His writing has been published on six continents and translated into 14 languages. He holds a PhD in Urban Studies from the Union Institute & University.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.003 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.007 |
| Insufficient payload (model declined to judge) | 0.016 | 0.004 |
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".