Historical spatio-temporal data on North American radical environmental direct-action events
Bibliographic record
Abstract
Social and political event data are widely used in scientific research. However, event data concerning the direct actions of radical environmental groups is comparatively scarce, due in large part to inconsistent news coverage and the clandestine nature of the groups involved. Leveraging original reports maintained by radical environmental groups and their allies, this article codes historical spatio-temporal event data on radical environmental direct-action events in the United States and Canada during a period of heightened prominence in radical environmentalism: 1995-2007. The article's event level data include information on event type, date and geolocation, and the target of each event, as well as the original textual reports of each coded event. This data will facilitate a wide variety of qualitative and quantitative analyses of radical environmental activism, alongside validations of recently developed large language model (LLM) tools for event data extraction. We also offer a separate spatio-temporally aggregated version of these same data. This second dataset is aggregated to the 0.5 × 0.5 decimal-degree spatial grid-year level and adds additional environmental-, environmental group-, and social-correlates. Accordingly, this second dataset will readily enable spatio-temporal statistical analyses of radical environmental direct-action events, their causes, and their determinants-phenomena that have been previously under-explored in large N studies.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.005 | 0.012 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 0.002 |
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".