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Record W1655375819 · doi:10.22230/cjc.2014v39n3a2830

Storylines in the Sands: News, Narrative, and Ideology in the Calgary Herald

2014· article· en· W1655375819 on OpenAlexaffvenueabout
Shane Gunster

Bibliographic record

VenueCanadian Journal of Communication · 2014
Typearticle
Languageen
FieldSocial Sciences
TopicClimate Change Communication and Perception
Canadian institutionsSimon Fraser UniversityUniversity of Ottawa
Fundersnot available
KeywordsIdeologyFraming (construction)NarrativeCriticismPetroleum industryCritical discourse analysisPolitical scienceContent analysisGovernment (linguistics)Media studiesPolitical economySociologyHistoryLawSocial scienceEngineeringPoliticsArchaeologyArtLiteratureEnvironmental engineering

Abstract

fetched live from OpenAlex

This article presents a critical discourse analysis of the principal storylines through which the Calgary Herald framed the oil sands between May 1, 2010, and May 31, 2011. The analysis reveals that rather than avoid coverage of environmental protests and critiques, the Herald’s narratives used these events to portray the oil and gas industry (and the province and people of Alberta) as victims of an aggressive and well-funded global environmental lobby. This framing not only defends the industry by dismissing environmental criticism of the oil sands as ill-informed and ideologically motivated, it also champions the idea that the provincial government must become a promotional petro-state whose main role is to actively defend the industry.

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)0.011
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 score0.675
Threshold uncertainty score0.646

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.011
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0030.003
Science and technology studies0.0230.023
Scholarly communication0.0130.004
Open science0.0010.005
Research integrity0.0040.006
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 head0.252
GPT teacher head0.406
Teacher spread0.154 · 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

Citations50
Published2014
Admission routes3
Has abstractyes

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