Ep 2 (rebroadcast) - "Petrocultures and the Energy Humanities" with Imre Szeman
Bibliographic record
Abstract
In the second episode of the "Climate and Capitalism" podcast series from GMU Cultural Studies, Amy Zhang talks with Imre Szeman, University Research Chair and Professor of Communication Arts at the University of Waterloo about his work individually and as part of the Petrocultures Research Cluster concerning oil, energy, and culture. This podcast series is associated with George Mason University Cultural Studies' Colloquium Series. This year's series is called "Climate and Capitalism." The industrial revolution liberated human beings from the cycles of nature — or so it once seemed. It turns out that greenhouse gases, a natural byproduct of coal- and petroleum-burning industries, lead to global warming, and that we are now locked into a long warming trend: a trend that will raise sea levels, enhance the occurrence of extreme weather events, and ultimately could threaten food supplies and other vital supports for modern civilization. This podcast series examines the cultural and political-economic dimensions of our ongoing, slow-moving climate crisis. We engage experts from a variety of fields and disciplines to ask questions about capitalism and the environment. How did we get into this mess? How bad is it? Where do we go from here? What sorts of steps might mitigate the damage — or perhaps someday reverse it? At stake are deep questions about humanity’s place in and relationship to nature — and what our systems of governance, production, and distribution might look like in the future. — Roger Lancaster, Colloquium Organizer Learn more about the Cultural Studies Program at GMU: http://culturalstudies.gmu.edu Learn more about the Petrocultures Research Cluster at University of Alberta: https://petrocultures.com/ Learn more about Imre Szeman: http://imreszeman.ca/ Music: Kevin MacLeod "Acid Trumpet," used under a Creative Commons Attribution 3.0 Unported License.
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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.004 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.004 | 0.004 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.014 | 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; both teacher heads agree on what is shown here.
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".