Marginal Returns?
Bibliographic record
Abstract
In the context of the rapid expansion of the natural gas economy around the globe, and the equally dramatic neoliberalization of governmental activity over the past three decades, we examine the impact of the LNG/CSG boom of the 2010s on institutional relationships within the rural communities and economies of the Peace River region of British Columbia, Canada, and the Surat Basin, Queensland, Australia. Conceptually influenced by staples theory and evolutionary economic geography, and drawing on intensive fieldwork conducted by the authors in both case study regions, we chart the socio-spatially uneven distribution of benefits, harms, and responsibilities associated with CSG expansion in both regions, focusing on the extent to which formal and informal government and governance institutions were able to overcome conditions of lock-in and inertia and capitalize on, and locally embed the benefits of, the boom. We find that while the sheer magnitude and pace of the boom initially caught formal and informal institutions off guard, over time some regional and local institutions generated multiscalar and horizontal relationships that enabled them to adapt and capture important and long-term economic benefits. Other regions, however, for a range of reasons, predominantly felt the negative consequences of CSG development, and felt largely abandoned by regional and central government institutions. Overall, our chapter reflects on the vital role of institutional relations and dynamics in shaping the evolving uneven geography of resource development.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.007 |
| Scholarly communication | 0.008 | 0.008 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.064 | 0.011 |
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".