Activist Networks, Territory, and the Spatial Diffusion of Mining Conflicts
Bibliographic record
Abstract
This article examines how activist networks contribute to the spatial diffusion of mining conflicts. We conduct a spatial econometric analysis of 590 geolocated mining properties in five countries of Latin America (Argentina, Brazil, Chile, Mexico, and Peru), using the univariate local joint count test and the spatial autoregressive probit method. Empirically, the analysis provides strong and generalizable evidence for the spatial diffusion of conflicts related to mining, namely that a social conflict in a given mine site can be considered an independent cause of a distinct social conflict in another proximate mine site that is additional to the localized factors considered in the quantitative and qualitative literature to date. Moreover, we show that the diffusion effect is most evident at a territorial level of analysis, which leads us to make the theoretical argument that the diffusion of collective action depends on the geospatial resonance of activist claims and mobilizing frames based on a common territorial experience among otherwise distant groups. The concept of geospatial resonance contributes to contemporary efforts to conceptually and empirically specify the limitations to a relational understanding of scale.
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 | 0.002 | 0.023 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".