Bibliographic record
Abstract
Introduction: High technology companies tend to cluster around knowledge-producing institutions (Braunerhjelm and Feldman, 2006). Aerospace follows this pattern, and there are well known examples of aerospace clusters located around large prime contracting aerospace producers (Niosi and Zhegu, 2005). A recent trend that is taking momentum is the setting up of manufacturing facilities by leading aerospace companies in Mexico. Objectives: The objective of this study is to investigate the attracting factors of Mexican clusters and to examine some of the country policy measures to incentive the aerospace sector, in order to assess the potential of Mexico to be a relevant part in this world-class high technology industry. Methods: A survey applied to selected aerospace firms in Mexico and interviews with regional governments are the information source. Results: Agglomeration forces in Mexican aerospace clusters are strongly related with manufacturing advantages. In addition, policy measures seem insufficient to encourage firms to undertake more complex activities. Conclusions: Even though advantages not related with innovation may explain the presence of firms in Mexico's aerospace clusters, these advantages should not be minimized given the tough quality standards of the aerospace industry, and if properly managed they may form the base for future development.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".