Humanizing hydrocarbon frontiers: the “lived experience” of shale gas fracking in the United Kingdom’s Fylde communities
Bibliographic record
Abstract
In this study, we explore the lived experiences of communities at the frontier of shale gas extraction in the United Kingdom. We ask: How do local people experience shale gas development? What narratives and reasoning do individuals use to explain their support, opposition or ambivalence to unconventional hydrocarbon developments? How do they understand their lived experiences changing over time, and what sorts of coping strategies do they rely upon? To do so, we draw insights from semi-structured interviews with 31 individuals in Lancashire, England, living or working near the only active shale gas extraction operation in the UK until the government moratorium was announced in December of 2019. Through these data, we identify several themes of negative experiences, including “horrendous” participation, community “abuse,” disillusionment and “disgust,” and earthquakes with the potential to “ruin” lives. We also identify themes of positive experiences emphasizing togetherness and community “gelling”, environmental “awareness,” everyday energy security with gas as a “bridging fuel,” and local employment with “high quality jobs.” Finally, we identify themes of ambivalent and temporally dynamic experiences with shale gas that move from neutral to negative regarding vehicle traffic, and neutral to positive regarding disgust with protesting behaviour and the diversion of community resources. Our study offers context to high level policy concerns and also humanizes community and resident experiences close to fracking sites.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.014 | 0.014 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.009 |
| Research integrity | 0.002 | 0.002 |
| 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".