Elves, environmentalism, and “eco-terror”: Leaderless resistance and media coverage of the Earth Liberation Front
Bibliographic record
Abstract
Over the past decade and a half, North America has seen a rash of environmentally motivated arsons. One group in particular, the clandestine Earth Liberation Front (ELF), has targeted ski resorts, genetic research labs, SUV dealerships, and forestry buildings, leading James Jarboe of the FBI to declare the ELF the “number one” domestic terrorist threat facing the USA. This article analyses the social construction of the “ecoterrorist threat” in the pages of the New York Times. Various stakeholders—including ELF spokespersons, moderate environmentalists, corporate interests, and state agencies—have sought to influence the way that media covers the ELF. Ultimately, much to the chagrin of ELF spokespersons, discourses of ecoterrorism have normalized in mainstream media, which regularly frames the spokespersons and activists as “dangerous clowns.” In turn, this coverage has prevented the expression of the ELF’s ideology, foreclosing the potential for the mainstream media to represent as legitimate the concerns of the ELF. I argue that blame for this failure rests in part with certain implications of the ELF’s organizational strategy of “leaderless resistance,” which—unlike civil disobedience movements of the past—is predicated on having its actors remain unsympathetically faceless and nameless.
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 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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.010 | 0.023 |
| Scholarly communication | 0.012 | 0.010 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.005 | 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".