Pipelines, Petitions, and Protests in the Internet Age: Exploring the Human Geographies of Online Petitions Challenging Proposed Transcontinental Alberta Oil Sands Pipelines
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.005 | 0.006 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".