What Technological Capabilities Do Manufacturing Companies Need for the Coordination of an Automotive Cluster?
Bibliographic record
Abstract
The objective of this research is to carry out an exploratory study of the technological capabilities and networks of a cluster of the automotive sector with companies in the Laja-Bajío region of the State of Guanajuato, Mexico. We applied factorial analysis of variables, found correlations and descriptive statistics of data that allow us to present the first findings for the sector. We determine whether these variables affect competitiveness and contribute to the medium-term coordination of an automotive sector cluster. Based on the results, specific recommendations are presented to improve technological capabilities and networks of the companies studied and those that belong to the manufacturing industry in the Laja-Bajío region. In order to carry out the study, we applied a questionnaire to participants of a 3rd annual SAPURAIYA industrial fair 2014, located in the city of Celaya, Guanajuato. Statistical techniques were applied to a conventional sample of 48 companies in the field.
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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 | 0.004 |
| 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 | 0.002 | 0.001 |
| 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".