Factors that Determine Work Precariousness - The comparison between the National and Foreign Direct Investment Industries:The case of Celaya Guanajuato México
Bibliographic record
Abstract
In 2012, the reform of the Federal Labor Law was implemented in Mexico, arguing that it was necessary to make the legislation more flexible, in order to position Mexico as a viable country to attract Foreign Direct Investment (FDI). However, there has been little monitoring on the real effects of these changes. The objective of this research was to analyze the factors that determine the precarious working conditions in the city of Celaya Guanajuato Mexico with the use of a quantitative approach of a non-experimental cross-sectional design. The results identified that precarious working conditions are correlated with the gender, age, schooling, economic sector and origin of the investment. The implications for management are: The legal flexibility for attracting direct foreign investment according to this research, has been an appropriate strategy, since it pressures labor markets to improve their conditions to be competitive, because the industry with direct foreign investment is governed by the Law, on the other hand, the national industry reflects labor practices well below the legal minimums.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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 source (direct Gemma or distilled Codex), 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".