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Record W4408406240 · doi:10.22230/cjc.2017v43n1a3316

Framing the Pipeline Problem: Civic Claimsmakers and Social Media

2018· article· en· W4408406240 on OpenAlexafffundvenueabout
Maria Bakardjieva, Mylynn Felt, Rhon Teruelle

Bibliographic record

VenueCanadian Journal of Communication · 2018
Typearticle
Languageen
FieldComputer Science
TopicHate Speech and Cyberbullying Detection
Canadian institutionsUniversity of Calgary
FundersSocial Sciences and Humanities Research Council of Canada
KeywordsFraming (construction)Political scienceSociologyMedia studiesGeography

Abstract

fetched live from OpenAlex

Background This article responds to the need for additional research into the role that social media play in the debate on energy transition in Canada. Analysis Based on a qualitative case study of the most recent protests against the Kinder Morgan pipeline project, this article raises questions concerning the strengths and weaknesses of the contemporary communication opportunity structure for “claimsmaking” (as Joel Best defines it in Social Problems) and achieving public resonance by the civic grassroots in Canada. Conclusions and implications This article investigates the ways in which social media have become a site for framing collective action by pipeline opponents. It documents how citizens and civic organizations combine online and offline tools and tactics to take part in the shaping of public understanding of pipeline projects in Canada and in the influencing of energy policy and decision-making.

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)0.009
metaresearch head score (Gemma)0.016
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.504
Threshold uncertainty score0.985

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0090.016
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0050.004
Science and technology studies0.0230.041
Scholarly communication0.0230.011
Open science0.0020.010
Research integrity0.0040.005
Insufficient payload (model declined to judge)0.0100.000

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 head0.224
Teacher spread0.208 · 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 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

Citations1
Published2018
Admission routes4
Has abstractyes

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