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Record W1920261590 · doi:10.1111/anti.12179

The New Nature of Things? Canada's Conservative Government and the Design of the New Environmental Subject

2015· article· en· W1920261590 on OpenAlexaffabout
Jonathan Peyton, Aaron Franks

Bibliographic record

VenueAntipode · 2015
Typearticle
Languageen
FieldEngineering
TopicMining and Resource Management
Canadian institutionsQueen's UniversityUniversity of Manitoba
Fundersnot available
KeywordsDominionGovernment (linguistics)LegislatureIdeologySubject (documents)Articulation (sociology)Power (physics)Public administrationSeparation of powersSociologyEconomicsPolitical sciencePolitical economyLaw and economicsLawComputer sciencePolitics

Abstract

fetched live from OpenAlex

Abstract Since coming to power in 2006, Canada's government under Stephen Harper has worked to recalibrate federal regulatory, legislative and economic development frameworks as they overlap in the littoral zone of the environment. We argue that Harper's Conservative government is pursuing a totalizing strategy in reconfiguring the desired Canadian environmental subject. This strategy approaches an integrated design that eclipses the incremental strategic options most Canadian federal governments have understood themselves to be constrained by. This design's basic features include the discursive strategies employed to collapse “the environment” into a singular resource extraction paradigm, a programmatic concentration of power to the executive branch of the Canadian government, and a classical conservative ideology that associates environmental regulation and management with dominion over and improvement of national territory, to the exclusion of other frames and relations. We query the articulation of consent and certainty in relation to the environment and extractive economies in Canada.

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)0.006
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: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.863
Threshold uncertainty score0.991

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0060.006
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.003
Science and technology studies0.0240.051
Scholarly communication0.0140.003
Open science0.0010.004
Research integrity0.0020.003
Insufficient payload (model declined to judge)0.0040.000

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 head0.006
GPT teacher head0.162
Teacher spread0.156 · 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

Citations42
Published2015
Admission routes2
Has abstractyes

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