Debating Extractivism: Stakeholder Communications in British Columbia’s Liquefied Natural Gas Controversy
Bibliographic record
Abstract
Shale gas extraction via hydraulic fracturing has been a controversial issue in many countries. In Canada, the provincial government of British Columbia (BC) has made relentless efforts on developing a liquefied natural gas (LNG) industry targeting potential Asian importers, which has been a heatedly debated public controversy since late 2011. Focusing on the two contending discourse coalitions formed by this policy initiative’s supporters and opponents, respectively, this article explores the intricate economic, political, and ideological struggles underlying Canadian extractivism. A qualitative discourse analysis of related stakeholder communications reveals that the pro-LNG coalition led by the BC Liberal government developed a “progressive extractivism” storyline to frame LNG exports as an unprecedented and ethical economic opportunity deserving the political support of environmentally minded British Columbians. By contrast, the anti-LNG coalition formed by progressive civil organizations, Indigenous groups, and concerned citizens engaged in fierce discursive resistance, notably via (a) adopting mainstream economic knowledge to highlight the fragile economic basis of BC LNG and (b) incorporating potent political issues such as democratic governance and reconciliation to expand public debates beyond the “jobs versus the environment” dichotomy.
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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 | 0.007 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.054 | 0.028 |
| Scholarly communication | 0.012 | 0.005 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.005 | 0.006 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
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".