Profile of the companies participating in the Mexican national exports award
Bibliographic record
Abstract
Objective: To identify the profile of the companies participating in the Mexican National Exportation Award (NEA) in the Large Agricultural Exporting Companies category (LAEC), by an information-gathering tool to determine the commercial lines of those businesses, their state of origin, and the exports destination. Methodology: a total of 17 questionnaires (n = 17), applied by the NEA to the LAEC category participants during the 2010-2018 period, were analyzed to determine the commercial business lines, their state of origin, and the destination of the exports. A problem tree was created to find opportunity areas to design solution proposals. The collected information was processed in the NetDraw 2.097 software to show the networks, their dominant actors (countries to which they export), and the products that the companies exported the most. Results: pork and vegetables business lines were identified. The latter revealed a sub-network of tomatoes and strawberries. A network was generated with an open structure comprising 17 nodes and 46 links where three export destination countries stood out: the USA with 15 links, Canada with six, and Japan with five. The highest exported product was the tomato in its different varieties, mainly to the U.S. and Canada. Limitations: Scarce information about the award on the internet. Access restrictions. Most of the exporting companies did not respond to the survey. Conclusions: the perishability of exported products determines the number of destination countries. The precariousness of Mexican agricultural exports was identified because companies trade only one product or a reduced number of them to only one country.
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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.006 | 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; 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".