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Record W4403314389 · doi:10.1515/9780228023388

The Rough Poets

2024· book· en· W4403314389 on OpenAlexaboutno aff
Melanie Dennis Unrau

Bibliographic record

VenueMcGill-Queen's University Press eBooks · 2024
Typebook
Languageen
FieldArts and Humanities
TopicEcocriticism and Environmental Literature
Canadian institutionsnot available
Fundersnot available
KeywordsArt

Abstract

fetched live from OpenAlex

Oil workers are often typecast as rough: embodying the toxic masculinity, racism, consumerist excess, and wilful ignorance of the extractive industries and petrostates they work for. But their poetry troubles these assumptions, revealing the fear, confusion, betrayal, and indignation hidden beneath tough personas. The Rough Poets presents poetry by workers in the Canadian oil and gas industry, collecting and closely reading texts published between 1938 and 2019: S.C. Ells’s Northland Trails , Peter Christensen’s Rig Talk , Dymphny Dronyk’s Contrary Infatuations , Mathew Henderson’s The Lease , Naden Parkin’s A Relationship with Truth , Lesley Battler’s Endangered Hydrocarbons , and Lindsay Bird’s Boom Time . These writers are uniquely positioned, Melanie Dennis Unrau argues, both as petropoets who write poetry about oil and as theorists of petropoetics with unique knowledge about how to make and unmake worlds that depend on fossil fuels. Their ambivalent, playful, crude, and honest petropoetry shows that oil workers grieve the environmental and social impacts of their work, worry about climate change and the futures of their communities, and desire jobs and ways of life that are good, safe, and just. How does it feel to be a worker in the oil and gas industry in a climate emergency, facing an energy transition that threatens your way of life? Unrau takes up this question with the respect, care, and imagination necessary to be an environmentalist reader in solidarity with oil workers.

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)0.001
metaresearch head score (Gemma)0.006
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.031
Threshold uncertainty score0.103

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.006
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0080.017
Scholarly communication0.0080.006
Open science0.0010.005
Research integrity0.0020.005
Insufficient payload (model declined to judge)0.0310.011

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 head0.012
GPT teacher head0.179
Teacher spread0.167 · 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 designNot applicable
Domainnot available
GenreOther

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
Published2024
Admission routes1
Has abstractyes

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