American Free Trade Agreement (NAFTA)
Bibliographic record
Abstract
Automotive production-relocation in the NAFTA region after the 2008 crisis is analyzed at different levels, including automobile unit-production changes in the regions within a Country, at the assembly-plant level and in the product portfolio. We compare production for two different years: 2007, when the crisis began and 2011 when the consequences could be observed. Based on information relative to where plants are located, year of establishment, firm ownership and production, we defined five regions: four in the US and Mexico and one in Canada. These regions were classified as “traditional ” or “emergent ” spaces. Results show that the age of each assembly plant was not a factor for restructuring. Traditional spaces in Mexico were the most favored, while in the USA traditional spaces were adversely affected. In the period under study, Canada decreased production by 400 thousand automobile units but still remained as the country with the best Economies of Scale in both years, manufacturing cars with high added-value: Canada was the only country that launched more new models than it stopped manufacturing. Mexico increased the average production per assembly plant during the crisis years, although it did not improve in Economies of Scale.
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 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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.021 | 0.005 |
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".