Inversión extranjera directa en industrias mexicanas intensivas en contaminación
Bibliographic record
Abstract
espanolEl objetivo del presente articulo fue analizar los flujos de inversion extranjera directa provenientes de Estados Unidos de America y Canada, dirigidos a industrias manufactureras mexicanas intensivas en contaminacion, periodo 1999- 2019. Utilizando los principales aportes de los investigadores Waldkirch y Gopinath (2008), Bakirtas y Atalay (2017), entre otros. Se tomaron cifras de flujos de inversion extranjera directa y la clasificacion de las industrias mas contaminantes, con un metodo de estadistica simple. Los resultados mostraron que, en cada pais el comportamiento difiere, Canada presenta escasa participacion mientras que EEUU lidera con alta presencia, en flujos con actividades manufactureras intensivas en contaminacion. EnglishThe objective of this article was to analyze the flows of foreign direct investment from the United States of America and Canada, directed to Mexican manufacturing industries intensive in pollution, period 1999-2019. The main contributions used were those of the researchers Waldkirch and Gopinath (2008), Bakirtas and Atalay (2017), among others. Figures on foreign direct investment flows and the classification of the most polluting industries were taken, using a simple statistical method. The results showed that, in each country the behavior differs; Canada presents little participation while the USA leads with a high presence, in flows with manufacturing activities intensive in contamination.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".