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Record W4250100825 · doi:10.1353/esc.2015.0064

Energy Humanities

2015· article· en· W4250100825 on OpenAlexvenueaboutno aff
Imre Szemán

Bibliographic record

VenueEnglish studies in Canada · 2015
Typearticle
Languageen
FieldArts and Humanities
TopicEcocriticism and Environmental Literature
Canadian institutionsnot available
Fundersnot available
KeywordsModernityEnergy (signal processing)PoliticsHumanitiesSociologyCultural studiesMedia studiesPolitical scienceSocial scienceHistoryAestheticsArtLawAnthropology

Abstract

fetched live from OpenAlex

Energy Humanities Imre Szeman When asked to name the event or scholarly work that has shifted the ground of theoretico-critical studies, I expect that most people will cast their gaze backwards. I’d like instead to look ahead, and to think about what we’re doing now that might have paradigm-altering consequences in coming years for critical work in the humanities. Studies of energy are just now blossoming within the greenhouse of the still-developing field called “energy humanities.” I think a critical encounter with energy will reshape how and why we undertake critical analysis. It constitutes a missing element in our understanding of the development of culture and society, including the shape of literature and of literary studies. And energy also belongs to our political vocabulary. Alongside the energy richness of modernity has been a corresponding energy unevenness that underlies the socio-political divide between global North and South. As we enter an era no longer defined by easy access to energy, struggles over energy resources are likely to define politics in the twenty-first century; they need to be part of the century’s literary and cultural studies, too. [End Page 21] Imre Szeman Professor and Canada Research Chair in Cultural Studies English and Film Studies University of Alberta Copyright © 2015 Association of Canadian College and University Teachers

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: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.895
Threshold uncertainty score0.398

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 communication0.0000.000
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 head0.057
GPT teacher head0.216
Teacher spread0.159 · 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 designNot applicable
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

Citations1
Published2015
Admission routes2
Has abstractyes

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