Comparative Analysis of Hydraulic Fracturing Wastewater Practices in Unconventional Shale Development: Newspaper Coverage of Stakeholder Concerns and Social License to Operate
Bibliographic record
Abstract
In this article we review prior literature regarding the concept of social license to operate, and related concepts, including corporate social responsibility, sustainable development, stakeholder management and cumulative effects. Informed by these concepts, we search for newspaper articles published in North American provinces and states where the Barnett, Duvernay, Marcellus and Montney shale plays are located. Using these data, we tabulate coverage of stakeholder concerns related to hydraulic fracturing and wastewater practices, and compare the extent to which these concerns vary over place and time. Our vocabulary analyses identify differences in the types and quantities of newspaper coverage devoted to concerns regarding hydraulic fracturing activities in general and wastewater practices in particular. We interpret these differences as suggesting that obtaining a social license to operate is likely not a one size fits all proposition. By understanding which stakeholder concerns are most salient in particular places and times, oil and gas operators and regulators can better tailor their strategies and policies to address local concerns. In other words, the findings from this study indicate that conventional understandings of risk as a technical or economic problem may not be adequate for dealing with unconventional resource challenges such as hydraulic fracturing. Operators and regulators may also need to manage social and cultural risks.
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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 | 0.002 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.005 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 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".