Capacidad de las exportaciones manufactureras para generar empleo y valor agregado interno en Norteamérica, 1995 - 2020
Bibliographic record
Abstract
The aim of this study is to estimate the employment and domestic value added generated by manufacturing exports in North America countries between 1995 and 2020. To achieve this, a multi-regional input-output model is applied, utilizing data from the Trade in Value-Added (TiVA-OECD) and Trade in Employment (TiM-OECD) databases.The results indicate that economies engage in manufacturing activities with different inputs to domestic indicators. In Mexico, exports are generators of employment, in the United States, they contribute to domestic value-added, and in Canada, the composition is intermediate. It is concluded that developed countries (the U.S. and Canada) participate in stages of greater generation of domestic value added. The originality and contribution of this document lies in the application of a widely used method for the analysis of the decomposition of value added to the employment variable.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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; both teacher heads agree on what is shown here.
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".