Looking through the prism of shale gas development: Towards a holistic framework for analysis
Bibliographic record
Abstract
In consideration of the size and geographic concentration of proved conventional gas reserves and the potential role of natural gas to reduce the carbon intensity of energy demand, unconventional gas resources have become increasingly important to expand natural gas supplies. Shale gas in particular has gained international relevance in recent years, largely due to its rapid development and game-changing effects in the United States and its wider and larger distribution worldwide over conventional gas reserves; nonetheless, developing shale gas in other countries has been much slower, as it presents increased risks that span multiple interlinked dimensions and differ across the perceptions of an ample array of stakeholders in diverse contextual settings. The premises presented in this paper attempt to advance a holistic framework for shale gas development which comprises several factors grouped in three major interlinked domains: access to natural resources, industry capabilities and governance. To empirically test its premises under contextual variations, the framework is further used to consistently analyze the cases of Canada, China and Mexico. Findings confirm the interdisciplinary nature of shale gas development and suggest that governance is the most critical domain to bring about changes that improve the management of underlying 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 | 0.004 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.003 | 0.017 |
| Scholarly communication | 0.010 | 0.010 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".