Mitigating Mistrust? Participation and Expertise in Hydraulic Fracturing Governance
Bibliographic record
Abstract
Abstract In Canada's Yukon Territory, a legislative committee was tasked with assessing the risks and benefits of hydraulic fracturing. The committee designed an extensive participatory process involving citizens and experts; however, instead of information access and public hearings fostering an open dialogue and trust, these two channels failed to de‐polarize debates over hydraulic fracturing. We argue that mistrust was reinforced because (1) weak participatory processes undermined the goals of public involvement, (2) scientific evidence and scientists themselves were not accepted as neutral or apolitical, and (3) strategic fostering of mistrust by actors on both sides of a polarized issue intensified existing doubt about the integrity and credibility of the process. The implications of a failure to restore trust in government are significant, not only for the issue of hydraulic fracturing, but for governance more broadly, as mistrust has spillover effects for subsequent public negotiations.
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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 | 0.057 | 0.102 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.007 | 0.026 |
| Scholarly communication | 0.008 | 0.007 |
| Open science | 0.002 | 0.011 |
| Research integrity | 0.005 | 0.003 |
| 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".