Burning bridges: why don’t organised crime groups pull back from violent conflicts?
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
Dominant theories of organised crime assume that criminal organisations which operate in extremely violent markets do so because they consider it financially cost-effective. This article contends that by using increasingly violent actions intended to deter competitors and government forces, criminal organisations sometimes eliminate their exit option, making the penalties for withdrawal to a less violent strategy significantly worse than those of continued violence. Based on a systematic examination of footage of public statements by 18 former associates of two Mexican organised crime groups (OCGs), La Familia Michoacana (LFM) and its offshoot Los Caballeros Templarios (LCT), this article argues that through gradual increases in their use of violence, these groups reached a ‘point of no return’. After reaching this point, desisting from further violence escalation became more hazardous than pursuing a violent path, even when the latter did not align with the organisations’ business interests.
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 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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