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Record W3108835796 · doi:10.1080/13549839.2020.1849076

Humanizing hydrocarbon frontiers: the “lived experience” of shale gas fracking in the United Kingdom’s Fylde communities

2020· article· en· W3108835796 on OpenAlexaff
Benjamin K. Sovacool, Laurence Williams, Abigail Martin, Jonn Axsen

Bibliographic record

VenueLocal Environment · 2020
Typearticle
Languageen
FieldEnvironmental Science
TopicAtmospheric and Environmental Gas Dynamics
Canadian institutionsSimon Fraser University
FundersEconomic and Social Research CouncilNatural Environment Research CouncilSight Research UK
KeywordsShale gasOil shaleEarth scienceGeologyEnvironmental protectionNatural resource economicsEnvironmental scienceEconomicsPaleontology

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.004
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.083
Threshold uncertainty score0.166

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.004
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0140.014
Scholarly communication0.0050.003
Open science0.0010.009
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0030.000

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.

Opus teacher head0.038
GPT teacher head0.224
Teacher spread0.186 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designQualitative
Domainnot available
GenreEmpirical

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".

Quick stats

Citations11
Published2020
Admission routes1
Has abstractyes

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