Sharpest Knives in the Drawer: Culture at the Intersection of Oil and State
Bibliographic record
Abstract
The paper presented, “’Sharpest Knives in the Drawer’: Culture at the Intersection of Oil and State”, is an expanded version of a chapter written for a book collection on Oil and Democracy currently under review with AUP (Eds. L Stefanick and M. Shrivasteva). The presentation also drew on previous work in the area of Alberta cultural history, and visual arts in particular. The text was supplemented for the presentation with slides of historical and contemporary works created with the oil industry as subject matter and as context of production. Compilation of this visual presentation helped to develop some of the ideas behind the theoretical framework and supplemented discussion to expand upon ideas. These ideas will be developed further in subsequent work on the topic. Conference participation was valuable in allowing new perspectives on the theme of the panel and on my own paper from an audience composed mainly of political scientists, which is not usually a primary area of my own research. Two of the audience members also approached me later with instances of oil industry visual culture that they had collected or remarked upon, which I will acquire for my project.
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 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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.008 | 0.013 |
| Scholarly communication | 0.009 | 0.005 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 0.000 |
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".