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Record W570941151

Directing discontent:differentiating between the consumption and contamination impacts of extreme energy projects

2013· article· en· W570941151 on OpenAlexaboutno aff
John Pearson

Bibliographic record

VenueLancaster EPrints (Lancaster University) · 2013
Typearticle
Languageen
FieldSocial Sciences
TopicSocial Acceptance of Renewable Energy
Canadian institutionsnot available
Fundersnot available
KeywordsContaminationConsumption (sociology)Energy consumptionNatural resource economicsEnvironmental scienceBusinessEconomicsSociologySocial scienceBiologyEcology
DOInot available

Abstract

fetched live from OpenAlex

Researching and writing on extreme energy and, in particular, the tar sands being extracted in Alberta, Canada, presents facts (both questionable and accepted) which shock and appall.However, upon consideration of the phenomenon in the broader contexts in which it undeniably resides, those of energy security, geopolitics and development to name but a few, realism about such projects inevitably emerges.These realities necessitate, paraphrasing conventional wisdom, acceptance of that which we cannot change, courage to change that which we can, and the wisdom to know the difference.The abandonment of the two high profile examples of 'extreme energy,' the tar sands, and fracking in the USA and UK, is therefore unlikely.Our reliance on hydrocarbons is undeniable and as conventional reserves dwindle, the allure of unconventional sources grows.Denying our inextricable connection to them for the foreseeable future is, objectively, remiss.Progress towards alternatives continues unabated but expecting developments to come to fruition and 'rescue' us from our daily need for hydrocarbons and derivatives thereof would be nave.As such, activists and environmental lawyers must accept that unconventional oil and gas extraction is inescapable in the short term.Essentially we are not able to break our 'addiction to oil.'

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.078
Threshold uncertainty score0.999

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 studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.050
GPT teacher head0.248
Teacher spread0.198 · 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 teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
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

Citations0
Published2013
Admission routes1
Has abstractyes

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