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Record W3019174547 · doi:10.1111/cag.12610

Wandering identities in energy transition discourses: Political leaders’ use of the “we” pronoun in Ontario, 2009–2019

2020· article· en· W3019174547 on OpenAlexaffvenueabout
Carelle Mang‐Benza, Carol Hunsberger

Bibliographic record

VenueCanadian Geographies / Géographies canadiennes · 2020
Typearticle
Languageen
FieldSocial Sciences
TopicSocial Acceptance of Renewable Energy
Canadian institutionsWestern University
Fundersnot available
KeywordsAmbiguityPronounPluralPoliticsScholarshipTransition (genetics)Normalization (sociology)Energy transitionPolitical scienceEnergy (signal processing)SociologyPolitical economyLinguisticsLawSocial science

Abstract

fetched live from OpenAlex

This paper explores the use of universalizing language as a discursive strategy to promote shifts in energy policy. Building on scholarship that seeks to understand the political nature of energy transitions, including resistance to transitions, the role of the state, and implications for justice, we examine three phases of energy transition in Ontario in the period 2009–2019, focusing on the ways that three successive ruling coalitions used the first plural pronoun “we” to promote contrasting energy policy orientations. Our analysis of policy documents and government news releases confirms that all three coalitions used the “we” form as a strategic device to define priorities, prescribe courses of action, and broadcast achievements. However, they also used the ambiguity of the “we” form to obscure alternative perspectives, claim credit for rivals’ accomplishments, and gloss over harmful and differentiated impacts of policy choices. The paper concludes by reflecting on broader questions about power and justice relevant to energy transition scholars.

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 categoriesMeta-epidemiology (narrow), Science and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.476
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.005
Science and technology studies0.0010.003
Scholarly communication0.0000.001
Open science0.0010.000
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 head0.023
GPT teacher head0.222
Teacher spread0.199 · 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.

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

Citations7
Published2020
Admission routes3
Has abstractyes

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