Grassroots Environmental Governance
Bibliographic record
Abstract
The “extractive frontier” has been the subject of much critical attention over the past decade. Scholars and activists broadly agree that the fossil fuel industry is making great efforts to extract fossil fuels from new sites and sources, often ones that had until recently been deemed inaccessible, because of technological challenges, political risks, costs, or a combination thereof (Bridge 2008; Bridge and Le Billon 2012). Many such sites and sources are for the moment described as “unconventional,” including deep offshore sources of oil and gas, oil sands in Canada and Venezuela, and shale oil and gas, although of course the unconventional becomes the conventional all too quickly. Many observers have suggested that the expansion of this extractive frontier has brought with it greater risks for society, the environment, and for the industry itself in certain respects: as extraction moves into more challenging physical situations, costs increase and the risks of accidents and the diffi culties of controlling them rise; as extraction moves into new locations, particularly in the global North, it becomes the subject of greater scrutiny and opposition by citizens and groups with more social power, particular legal rights, and often a greater willingness to consume fossil fuels than to live in the sites of their production.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.010 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.014 | 0.002 |
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".