Bibliographic record
Abstract
We would like to acknowledge all of the authors whose intellectual labour has made this collection possible and whose ongoing work is contributing in significant ways to the discipline of the energy humanities.Their work adds to the body of knowledge being created by the members of the Petrocultures Research Group and its affiliates: colleagues working on oil and energy-related issues in more than twelve countries around the world.We would also like to thank the more than one hundred participants in the first Petrocultures conference we hosted, in 2012 (Edmonton, Alberta), and the organizers and participants of Petrocultures 2014 and 2016 at McGill University (Montreal, Quebec) and Memorial University (St John's, Newfoundland).Now a biennial event, these gatherings are helping to build and shape the discipline, as well as to create a community of scholars and artists in dialogue with industry, government, and policy makers.This research will help to give shape to the energy transitions and social transformations of the coming century.A huge thanks to the sponsors of the original Petrocultures conference, which include the Kule Institute for Advanced Study (University of Alberta), Campus Saint-Jean (
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 | 0.005 | 0.031 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.278 | 0.176 |
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".