Bridging Troubled Waters: History as Political Opportunity Structure
Bibliographic record
Abstract
This article seeks to provide insight into the formulation of non-governmental organization (NGO) and transnational advocacy network (TAN) campaign strategy. We argue that the history of previous campaigns comprises an important aspect of the political opportunity structure faced by NGOs and TANs. We also argue that when formulating campaign strategy, campaigners should not only consider the legacies of previous campaigns, but also how their current strategies could impact on political opportunity structure and thereby influence future campaigns. This article uses the case study of the movement against seal hunting in Atlantic and Northern Canada and considers the potential for collaboration between previous opponents on other environmental issues. We examine the history of the anti-sealing campaigns looking at the various actors involved, and the impact that these campaigns had on these actors and their current relations with one another. The case study demonstrates that the history of previous campaigns matters and that history is a vital component of political opportunity structure.
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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 | 0.004 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.009 | 0.022 |
| Scholarly communication | 0.009 | 0.011 |
| Open science | 0.001 | 0.010 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 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".