“Beautiful Patterns 2019” MIT and Tecnologico de Monterrey high-impact IT/K12-STEM transnational initiative for young women students
Bibliographic record
Abstract
Intended for Mexico, the “Beautiful Patterns” young-women STEM bootcamp was created in 2017 at the Massachusetts Institute of Technology, finding a strategic partner in the Tecnológico de Monterrey, a recognized Mexican university. The idea behind “Beautiful Patterns” is promoting the STEM majors as a future, promising and country-needed option for high school women students in economies like Mexico's where only 17% of the graduated engineering population are women. In 2021, this bootcamp will become international, including instructors and participants from Canada and some Latin American, European and Asian countries. In 2019, after having taken part in this program, 89% of the student participants stated that they had a different perspective about engineering and information technologies recognizing women's potential in STEM areas. The 2020 edition was canceled due to the pandemic of CoVid-19. In this paper we present all our results and conclusions of the 2019 implementation in Aguascalientes Campus of Tecnológico de Monterrey.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.012 | 0.002 |
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".