Fragmenting Mexican cartels will focus on mining
Bibliographic record
Abstract
Subject The impact of organised crime on the mining sector. Significance Canada-based McEwen Mining said on May 11 that it was on track to meet annual production targets despite last month's theft of 900 kilos of gold concentrate from its El Gallo mine in Mexico. On April 6, eight heavily armed robbers burst into the mine in Sinaloa state and walked away with concentrate containing gold worth 8.5 million dollars -- equivalent to two-fifths of the mine's quarterly production. Suspicion inevitably fell on the local Sinaloa cartel. Company CEO Rob McEwen seemed to acknowledge that up to that point the mining company had cooperated with the cartel, putting the question of the relationship between mining companies and drug traffickers back on the table. Impacts Greater fragmentation of Mexican cartels will lead to increasing criminal targeting of mining businesses. Following the general pattern of insecurity, companies operating in Michoacan and Guerrero are most at risk. The April heist at the El Gallo mine suggests that more independent gangs are also now operating in Sinaloa. Mining company experiments with supporting self-defence organisations seem to have failed.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.003 | 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 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".