Fab Lab-Based Learning and Gender Gap in North America
Bibliographic record
Abstract
In this quantitative research, we analyze the geographic distribution and gender gap of women graduates from a Fab Lab-based learning environment across IEEE Regions 1–7 in North America. The United States (Regions 1–6) contributed 10% (N=143) of the women graduates, while Canada (Region 7) contributed 1 % (N=11) across 26 Fab Academy nodes out of 324 Fab Labs in Regions 1–7. The study includes 49 women graduates from 2 countries between 2009 and 2023, out of 154 graduate students. These results are compared to the total Fab Academy graduates by gender (N=1,433) for IEEE Regions 8–10 (Europe, Africa, the Middle East, Latin America, Asia, and Oceania). The United States has the highest number of women graduates in the Fab Academy universe at 33%, followed by Region 8 (30%), Region 10 (23%), Region 9 (20%) and Region 7 (18%). The study provides quantitative evidence supporting previous qualitative initiatives, where Fab-Lab-based learning directly and positively impacts a greater number of women who acquire STEM skills.
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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".