A Configurational Analysis of Civil Society Organizations in Extractive Conflicts
Bibliographic record
Abstract
Civil Society Organization’s (CSOs) knowledge and material assets can help communities elevate their environmental justice mobilizations against extractive companies. The strategic value of this assistance is, however, shadowed by the reality of increasing violence against mobilizing activists. These conflicts also threaten the firms involved with costly disruption to operations and a loss of reputation. This paper attempts to develop a deeper understanding of this contentious circumstance by addressing the question: How do the configurations of different types of CSOs present in community mobilizations affect the intensity of community conflicts against extractive firms? We perform a fuzzy-set QCA based configurational analysis of the key stakeholder groups present in 550 environmental justice conflicts against extractive firms, occurring worldwide between the years 1996-2017. Our variables of interest include CSOs (international and/or local), type of firm (foreign firm or not), type of community (rural or not, and variety of mobilizing groups) and Government (assessed in terms of its regulatory quality and level of economic dependence on extractives). Our results contribute to the literature on stakeholder influence strategies (Frooman, 1999) by portraying 10 unique configurations of stakeholders, which lead to high intensity of conflict and 5, which lead to low.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 teacher head, 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".