MétaCan
Menu
Back to cohort
Record W2611648487 · doi:10.1080/14660466.2017.1309884

‘If they only knew what I know’: Attitude change from education about ‘fracking’

2017· article· en· W2611648487 on OpenAlexaboutno aff
Darrick Evensen

Bibliographic record

VenueEnvironmental Practice · 2017
Typearticle
Languageen
FieldEnvironmental Science
TopicAtmospheric and Environmental Gas Dynamics
Canadian institutionsnot available
FundersEuropean Commission
KeywordsHydraulic fracturingQuarter (Canadian coin)PerceptionEnergy (signal processing)Class (philosophy)Public opinionPublic relationsPolitical sciencePsychologySociologyLawEngineeringPetroleum engineeringHistoryPoliticsComputer science

Abstract

fetched live from OpenAlex

A simple explanation for why another’s perspectives on unconventional gas development via hydraulic fracturing differ from one’s own is that people are uninformed. Such an answer employs the deficit model of communication and understanding—shown for a quarter century to be inadequate for explaining public perceptions and behaviors. A more likely explanation, but far more challenging for an easy “fix”, is that values fundamentally shape views. In autumn 2014, I taught an undergraduate course entirely on unconventional gas development (UGD) via hydraulic fracturing (often called “fracking”). I evaluated the effects of intensive education on attitudes about UGD by presenting my students with the same survey on the first and penultimate days of class. Overall attitudes changed little, despite substantial increases in self-reported knowledge and changes in beliefs about impacts associated with UGD. This poses a challenge for energy policies and regulation built off the assumption that additional education can readily change attitudes. I consider ways of approaching policy that respond to education’s limited effects on attitudes about UGD.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.754
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.001
Scholarly communication0.0010.009
Open science0.0010.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0060.007

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 head0.013
GPT teacher head0.266
Teacher spread0.253 · 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; both teacher heads agree on what is shown here.

Study designObservational
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

Citations12
Published2017
Admission routes1
Has abstractyes

Explore more

Same venueEnvironmental PracticeSame topicAtmospheric and Environmental Gas DynamicsFrench-language works237,207