Labor outsourcing in the mining-metallurgical industry in Mexico, 2003-2018: Territorial, sectoral and social evolution
Bibliographic record
Abstract
• From 2003 to 2018, subcontracted mining jobs in Mexico grew by 550 %. • In gold mining, 3 out of 4 workers were hired through subcontracting. • Big companies held 88 % of all subcontracted mining workers in 2018. • Most women entered mining jobs through subcontracting. • The anti-outsourcing law cut mining subcontracting from 42 % to 24 %. This study analyzes, at the national scale, the territorial, sectoral, and social evolution, and the magnitude of labor outsourcing in the mining-metallurgical industry in Mexico between 2003 and 2018, as well as its main implications for workers. Using census data, company reports, interviews, and document reviews, the analysis shows a 550.7 % growth in outsourced work in extractive mining, concentrated in precious metals and in states such as Colima, with little presence in the metallurgical industry. The consolidation of this model was driven by large companies that, taking advantage of regulatory flexibility and union fragmentation, managed to reduce costs at the expense of lower wages, instability, and loss of labor rights of thousands of workers. Likewise, this study documents the extreme use of outsourcing as a strategy to incorporate women into mining, which accentuated specific barriers related to motherhood, workplace violence and unequal access to training and promotion, thereby reproducing structural gender inequalities. In also reveals a marked asymmetry in the use of this scheme between companies operating in Mexico, Peru, Canada, and the United States: in more institutionally flexible contexts, outsourcing is widely used, whereas in settings with stricter regulation and oversight its use is considerably lower. It is concluded that outsourcing contributed to the increase in precariousness among mine workers and that the 2021 labor reform was an effective response to its abuses, although mechanisms of labour precarisation persist and require complementary policies to strengthen labor justice.
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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 | 0.001 | 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 | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 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".