Chaos from order: a network analysis of in-fighting before and after El Chapo’s arrest
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Abstract The effect of leadership decapitation—the capture or killing of the leader of an armed group—on future violence has been studied with competing conclusions. In Mexico, leadership decapitation has been found to increase violence and in-fighting among drug cartels. However, the causal pathways between leadership decapitation and in-fighting are unclear. In this article, it is hypothesized that leadership decapitation will weaken alliances between armed actors, lead to greater preferential attachment in networks of cartels and militias, and result in greater transitive closure as cartels seek to expand their power. These hypotheses are tested with a stochastic actor oriented model on a network dataset of episodes of infighting among cartels and the militias formed to opposed them between the five years before and after Joaquín, “El Chapo” Guzmán Loera, the former leader of the Sinaloa Cartel, was arrested in 2016. The results show that alliances have virtually no effect on the decision of cartels and militias to fight each other; weaker organizations faced a higher reputational cost after El Chapo’s detention; and post-arrest cartel in-fighting did not increase as a result of uncertainty about the relative balance of power among cartels.
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.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it