Feature Story: Digging deep into the fossil fuel industry
Bibliographic record
Abstract
The fossil fuel industry in Western Canada is coming under the microscope in a multi-million dollar research project that includes researchers from the University of Regina. The Social Sciences and Humanities Research Council has awarded a 6-year, $2.5 million partnership grant hosted by the University of Victoria, and jointly led by UVic, the Canadian Centre for Policy Alternatives’ Saskatchewan and BC Offices, and the Parkland Institute at the University of Alberta. The project titled, “Mapping the Power of the Carbon-Extractive Corporate Resource Sector,” brings together various groups and individuals that will study the oil, gas and coal industries in British Columbia, Alberta and Saskatchewan. The Saskatchewan team includes Dr. Emily Eaton, Associate Professor in the Department of Geography and Environmental Studies; Dr. Andrew Stevens, Assistant Professor in the Faculty of Business Administration; Dr, Angela Carter from the University of Waterloo and Dr. Simon Enoch, Adjunct Professor at the Faculty of Graduate Studies and Research at the U of R.
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 | 0.002 |
| Science and technology studies | 0.016 | 0.005 |
| Scholarly communication | 0.009 | 0.006 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.004 | 0.008 |
| Insufficient payload (model declined to judge) | 0.033 | 0.005 |
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".