Bibliographic record
Abstract
Greenpeace was founded in Vancouver in the early 1970s. Initially, it was a small anti-nuclear protest group composed of Americans and Canadians, peaceniks and hippies, World War II veterans and people barely out of high school. Twenty years later, it was the world’s largest environmental NGO, with headquarters in Amsterdam, branches in over forty nations, and a regular presence at international environmental meetings throughout the world. This article will chart Greenpeace’s growth throughout its first two decades, in the process examining how the organization became influential at several levels: in local politics in places like Vancouver; at the national level in countries such as Canada, New Zealand, the USA, and Germany; and at global forums such as the International Whaling Commission and various UN-sponsored environmental meetings. It will analyze the combination of activist agency and political op-portunity structures that enabled Greenpeace to gain political influence. I argue that Greenpeace’s influence largely stemmed from its engagement with what political scientist Paul Wapner calls “world civic politics,” which in this case involves the dissemination of an ecological sensibility that indirectly influences behavior at multiple scales, from individuals, to governments, to multi-lateral organizations. Only in this way could a group with relatively limited resources hope to influence millions of individuals and powerful governments.
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 | 0.002 | 0.003 |
| Science and technology studies | 0.005 | 0.014 |
| Scholarly communication | 0.010 | 0.004 |
| Open science | 0.001 | 0.013 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.010 | 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".