Legal opportunity structure and social movement strategy in Northern Ireland and southern United States
Bibliographic record
Abstract
The Civil Rights Movements in the southern United States and Northern Ireland were able to mobilize African Americans and Irish Catholics respectively against minority discrimination. These movements initially displayed very similar goals and tactics, looking at courts to counter institutional discrimination, but in successive stages of contention their trajectories fundamentally diverged. While legal mobilization in the United States constituted one of the pillars of the civil rights strategy of contention, in Northern Ireland legal tactics were supplanted by a transgressive (and at times violent) repertoire of contention. To explain this discrepancy, this article relies on the concept of legal opportunity structure (LOS) as an analytical tool to investigate how a state’s legal structure affects social movement legal mobilization. Accessibility to courts, availability of justiciable rights and judiciary receptivity are identified as the three core dimensions of the LOS shaping its degree of openness/closure. The paired comparison of these movements reveal that a closed LOS may narrow down the array of tactical options available to social movements, redirecting activists’ efforts towards protest. Conversely, an open LOS may encourage legal mobilization as a viable tactical option and, under certain circumstances, even promote contentious activities.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.004 | 0.010 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".