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Record W7027909547

The duality of man: An exploration of masculine identities as both drivers and disruptors of far-right climate denialism in Canada

2024· other· en· W7027909547 on OpenAlexaboutno aff

Bibliographic record

VenueLund University Publications Student Papers (Lund University) · 2024
Typeother
Languageen
FieldSocial Sciences
TopicSoviet and Russian History
Canadian institutionsnot available
Fundersnot available
KeywordsPoliticsHegemonyRhetoricHumanityNeoliberalism (international relations)Climate changeState (computer science)Power (physics)Feminism
DOInot available

Abstract

fetched live from OpenAlex

The climate is in a state of emergency. Anthropogenic climate change is destroying social and environmental systems across the world (albeit, unevenly) and jeopardizing the safety and humanity of present and future generations. Nevertheless, climate denialism persists as a rhetoric and belief, particularly within the burgeoning far-right political projects in countries most responsible for rising temperatures. The current study endeavours to unearth why this may be the case, and how it could potentially be explained by another facet of far-right ideology: misogyny. To do so, I critically analyze the People’s Party of Canada’s climate discourse through a political ecology lens anchored in Gramscian theories of hegemony and feminist conceptualisations of masculinities. Ultimately, I find that the PPC articulate a heavily masculinized and topically diverse form of denial, assessed to be a part of a broader strategy to secure power within Canadian politics via established dominant fossil fuel and patriarchal paradigms. I suggest several strategies that could contribute to the effective resistance of such a complex discourse, including the elevation of ecological masculine identities and an unwavering endorsement of multiculturalist ethics.

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)0.004
metaresearch head score (Gemma)0.004
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: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.120
Threshold uncertainty score0.872

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.004
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0030.004
Science and technology studies0.0460.029
Scholarly communication0.0120.003
Open science0.0020.006
Research integrity0.0020.004
Insufficient payload (model declined to judge)0.0050.000

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 head0.013
GPT teacher head0.255
Teacher spread0.241 · 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

Citations0
Published2024
Admission routes1
Has abstractyes

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