Igniting the Innovation’s Competencies at Engineering Schools: IoT to the Cloud Labs Network in Mexico
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it