Bibliographic record
Abstract
As a contribution to the growing exploration of oil and energy in the humanities, the author examines what we might learn from three attempts to probe how we know oil—that is, the complex, myriad ways in which we try to name and narrate oil's social significance—in order to understand better the opportunities and challenges of making oil and energy a more conceptually powerful part of our social and cultural understandings. The first of the energy epistemologies the author examines, Timothy Mitchell's Carbon Democracy (2011), reframes the history of left politics in relation to shifts in dominant forms of energy. The second, Edward Burtynsky's photo-series Oil (2011), identifies the deep social significance of oil through experiments in visual form. The third example of knowing oil and energy is the ongoing struggle over the representation of the Alberta oil sands in public and political debate and discussion. The intent of examining these three distinct attempts to know oil as an essential component of social, cultural, and political life is to see what lessons such energy epistemologies might have for a left politics committed to an energy transition that would both ameliorate environmental concerns and enable greater social justice.
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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 | 0.005 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.012 | 0.075 |
| Scholarly communication | 0.015 | 0.023 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".