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Record W2624354784 · doi:10.1080/24694452.2017.1320212

Pipelines, Petitions, and Protests in the Internet Age: Exploring the Human Geographies of Online Petitions Challenging Proposed Transcontinental Alberta Oil Sands Pipelines

2017· article· en· W2624354784 on OpenAlexaboutno aff
Jodi L. McNeill, Thomas F. Thornton

Bibliographic record

VenueAnnals of the American Association of Geographers · 2017
Typearticle
Languageen
FieldSocial Sciences
TopicSocial Media and Politics
Canadian institutionsnot available
Fundersnot available
KeywordsCollective actionPoliticsIncentiveGeospatial analysisPolitical scienceThe InternetPipeline transportSociologyPublic relationsEconomic geographyPolitical economyGeographyLawEngineeringEconomics

Abstract

fetched live from OpenAlex

Since the mid-2000s, millions of spatiotemporally disparate and demographically heterogeneous North Americans have signed online petitions challenging proposed transcontinental Alberta oil sands export pipelines. This phenomenon typifies bottom-up, self-organized, and ostensibly extemporaneous cyberactivism. These dynamics contradict traditional theoretical assumptions about rational choice and social pressures in collective action, birthing queries regarding why individuals participate. Human geographies comprising three online petitions challenging separate proposed pipelines are accordingly examined by comparing signatories' stated sociopolitical motivations for signing with their corresponding geospatial distributions. This innovative fusion of qualitative and quantitative research methods was designed to explore hetero versus homogeneity in signatories' sociopolitical commitments and locations. The results empirically corroborate Bennett and Segerberg's (2012) thesis that cyberactivism is governed by a unique logic of connective action wherein participation thresholds are low, collective identities and social incentives are weak, relationships are defined socially rather than spatially, and contentious politics are highly personalized. Four integrated findings with implications for policymaking and future research are offered for consideration.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.036
Threshold uncertainty score0.973

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.002
Scholarly communication0.0000.000
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 head0.063
GPT teacher head0.351
Teacher spread0.288 · 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.

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

Citations9
Published2017
Admission routes1
Has abstractyes

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