Non-violence and Activism: The Case of Greenpeace’s Anti-Sealing Campaign
Bibliographic record
Abstract
This paper explores the question: What does Greenpeace’s practice of non-violence during its anti-sealing campaigning reveal about the organization’s relationship with the philosophy? It highlights the complexity of what non-violence is, focusing on Greenpeace’s experience of navigating claims that it is a principled non-violent organization when its legacy of anti-sealing activism reveals that it is much more pragmatic than it would like the public to recognize. Using interviews and archival research, the paper unpacks Greenpeace’s approach to Indigenous and non-Indigenous sealers, while it navigated its own internal tensions, inherent classism, and attitudes within the wider anti-sealing movement. It argues that Greenpeace’s commitment to its reported non-violent principles and taking accountability for when it errs in its activism, really depends on whether Greenpeace can afford to ignore those they negatively impact or whether there are social, legal, and strategic factors that incentivize taking responsibility and making changes.
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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 | 0.005 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.033 | 0.026 |
| Scholarly communication | 0.010 | 0.005 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.006 | 0.007 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".