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Record W4239694871 · doi:10.1108/oxan-db199508

Fragmenting Mexican cartels will focus on mining

2015· other· en· W4239694871 on OpenAlexaboutno aff

Bibliographic record

VenueEmerald expert briefings · 2015
Typeother
Languageen
FieldSocial Sciences
TopicCrime, Illicit Activities, and Governance
Canadian institutionsnot available
Fundersnot available
KeywordsCartelMining industryFellOrganised crimeProduction (economics)BusinessGold miningPolitical scienceEconomyLawEngineeringGeographyEconomicsCollusionIndustrial organizationMining engineeringCartography

Abstract

fetched live from OpenAlex

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.

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 distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.643
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0030.000

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.

Opus teacher head0.031
GPT teacher head0.309
Teacher spread0.278 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designNot applicable
Domainnot available
GenreOther

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".

Quick stats

Citations0
Published2015
Admission routes1
Has abstractyes

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