Exploring the interface of environmental activism and digital surveillance
Bibliographic record
Abstract
The topic of this paper is the emergent issue of surveillance of environmental activists through new and social media, as interests that potentially threaten the ‘security of the state’. The latter is a frame that emerged post–9/11 to revise surveillance of criminal activities to also include the activities of social movements, including environmental activists. Following a background on environmental activism and surveillance, we find new and social media in contexts that enable both environmental activism and digital surveillance. In regard to the latter, we explore the concept of ‘ecoterrorism’, which frames certain understandings of environmental activism as acts of terrorism. We then briefly refer to recent cases of digital surveillance of environmental campaigners in Canada, Pennsylvania, and Australia. Finally, we investigate the extent to which digital surveillance may influence the protest activities of environmental activists, and how environmental activists (and everyday citizens) respond to surveillance. Summing up, we reflect first on the potential of digital surveillance to curb environmental activism with its aim to protect the environment and move towards strong sustainability and green economies; and second, on the potential of environmental activism to resist or manage surveillance.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.004 | 0.016 |
| Scholarly communication | 0.010 | 0.013 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.009 | 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".