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Record W2741010723 · doi:10.1111/cag.12392

Place‐making at a national scale: Framing tar sands extraction as “Canadian” in<i>The Globe and Mail</i>

2017· article· en· W2741010723 on OpenAlexvenueaboutno aff
Toby Davine, Mary Lawhon, Joseph R. Pierce

Bibliographic record

VenueCanadian Geographies / Géographies canadiennes · 2017
Typearticle
Languageen
FieldSocial Sciences
TopicGeographies of human-animal interactions
Canadian institutionsnot available
Fundersnot available
KeywordsFraming (construction)IndigenousHegemonyGlobeNationalismMedia studiesNarrativeNewspaperSociologyOpposition (politics)Political economyPolitical scienceLawHistoryPoliticsArchaeology

Abstract

fetched live from OpenAlex

Although the concept of place has most often been used to examine micro‐scale locales, recent explications of place‐making and place‐framing can usefully inform debates on nations, nationalism, and the nation‐state. Viewing the nation as a contested, unstable, and relational place enables a pluralist and dynamic understanding of how nation‐places are constructed and contested, by whom, and towards what ends. In this paper, we examine public debates over the extraction of the Canadian tar sands as an illustrative example of how place is negotiated, deployed, and contested to legitimize particular outcomes. We analyzed 50 articles from one of Canada's most widely circulated daily newspapers, The Globe and Mail. We found diverging place‐frames of Canada used by government, industry, Indigenous groups, environmentalists, and other stakeholders as they make cases for and against the development of the tar sands. Framings of Canada promoted by the government and industry—Canada as a modern, rational, and legitimate actor—featured most prominently in the sample. Importantly, however, counter‐frames trouble narratives of Canada's inherent benevolent and responsible nature, and offer a small, yet strong opposition to hegemonic national imaginaries.

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)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Science and technology studies, Scholarly communication
Consensus categoriesScience and technology studies
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.387
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.001
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0060.002
Science and technology studies0.0130.005
Scholarly communication0.0020.001
Open science0.0020.000
Research integrity0.0000.001
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 head0.016
GPT teacher head0.284
Teacher spread0.267 · 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; both teacher heads agree on what is shown here.

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

Citations15
Published2017
Admission routes2
Has abstractyes

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