Bibliographic record
Abstract
It is a myth that books are a product of a solitary individual working alone at a desk.They require the effort, and forbearance, of a lot of people for a long period of time.This book is no exception: you could say it was decades in the making.My interest in oil started long ago, growing up as I did in Alberta, Canada, in the 1970s and 1980s.The province's economy waxed and waned with the fortunes of the industry.More memorable were the people: enthusiastic, optimistic, energetic, and independent were the key adjectives describing their attitudes about where they were and what they were doing.So that is a great place to begin: I had the good fortune to start life where, when, and with whom I did, which planted oil and politics firmly in my worldview.In the decades since I have benefited from a very large number of people who contributed in ways large and small to this finished product.There are too many to be listed and thanked properly, but here is at least my best attempt.In my university years, both undergraduate and graduate, I had professors and mentors who showed me how politics and economics were inherently interrelated.Studying the discipline of political economy in graduate school gave me chances to explore these links myself.There I was also introduced to the community of scholars who also have spent much of their working life exploring the intricacies of oil and the political problems it generates.Their work has influenced mine greatly and became the foundation for what is explored in the
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 | 0.004 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.378 | 0.270 |
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; the direct Gemma label and the distilled Codex classifier 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".