Igniting the Innovation’s Competencies at Engineering Schools: IoT to the Cloud Labs Network in Mexico
Bibliographic record
Abstract
Learning and innovation’s skills are increasingly recognized as key factors separating students who are prepared formore complex environments of life and work in the twenty-first century, and those who are not. The relationshipbetween the industry and the academia is undoubtedly in Mexico and several countries nowadays a very importantsocial and institutional phenomenon. Academy and Industry have always been cooperating in a win-win manner. Overtime, this relationship has evolved in many mechanisms where learning skills developed strongly, but at present,innovation skills are taking more relevance. Efforts like an “IoT to the Cloud Innovation Labs Network” implementedby the Intel® Guadalajara Design Center in Mexico are contributing to foster the innovation’s competencies and skillsfrom students and have been having a profound impact at the local ecosystem at each one of the states where these labsare established. As part of the results, this labs network has been bringing more than 200 innovative projects, indifferent areas like smart agriculture, Internet of Things, automation, wearables, smart hearth, and robots, amongothers. Additionally, more than 3200 people (students, teachers, individuals from the industry and government) havebeen receiving some training coming from this labs network. All the courses and workshops have been deployed in atrain the trainers’ model, bringing a strong, scalable possibility and impact, to the local ecosystems and each one of thestates.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".