Shale development in the US and Canada: A review of engagement practice
Bibliographic record
Abstract
Public and stakeholder engagement with shale development is difficult, but essential. We review 26 engagement processes carried out by US and Canadian companies, alliances, government agencies, academics and activists; systematically exploring who participates, the stage at which engagements take place, aims and methods, provision for multiway engagement, and issues of credibility. We find a multitude of actors carrying out engagement using a variety of formats, ranging from barbeque events and town hall meetings to citizen science and in-depth qualitative research. Whilst we find many strengths, we also highlight a number of weaknesses. Much of this engagement does not occur at the earliest stages of development, and rarely asks the most fundamental question -whether shale development should proceed at all- instead commonly focusing on questions of impact minimisation, regulation and gaining support. Furthermore, the majority of activities tend to elicit the responses of interested and affected parties, with much less attention to views of the wider public. We reflect on what may be limiting engagement practice, and discuss how engagement might be improved.
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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 | 0.009 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.012 | 0.029 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| 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".