Dissecting Dissent: A Multilevel Integrative Framework on Nonviolent Resistance
Bibliographic record
Abstract
Research into nonviolent resistance movements has surged over the past decade. The successes and failures of these movements has been the main focus of academic interest, with the explorations of why and how these movements emerge fading into the background. The aim of this paper is to survey and synthesise the field and create a multilevel systematic framework of factors that impact the onset and effectiveness of nonviolent resistance movements. In total, over forty-one factors relating to the structure of society and the agency of the resisters have been identified. These factors have been placed into seven categories which belong to three levels of analysis. On a macro-, meso- and micro-level, these categories include factors relating to the political and economic system, modernization, the country’s international relations, the balance of power within society, and the grievances, resources and capacity of the resistance group. The presented framework allows scholars to systematically analyse nonviolent resistance cases in the future, which contributes to assessing the relevance and weight of the identified factors.
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 | 0.045 | 0.067 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.012 | 0.007 |
| Science and technology studies | 0.014 | 0.121 |
| Scholarly communication | 0.028 | 0.034 |
| Open science | 0.007 | 0.019 |
| Research integrity | 0.008 | 0.008 |
| 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".