Engineering Education through the Latina Lens
Bibliographic record
Abstract
<p class="Body">Less than 20% of undergraduates earning a degree in engineering are women, and even more alarming is minority women earn a mere 3.1% of those degrees. This paper reports on a qualitative study examining Latinas’ identity development toward and in undergraduate engineering and computer science studies using a sociocultural theory of learning. Three major themes emerged from the data analysis: 1) Engineering support clusters as affinity spaces contributing to development of engineering identities; 2) Mexican or Mexican-American family contributing to persistence in engineering; and 3) Equity in access to engineering education. Engineering support clusters and Mexican heritage family support were vital in developing and sustaining Latinas’ engineering identity. Additionally, data supported the idea that Latinas at the research site experienced gender and ethnic equity in their access to engineering education. The authors call for a more gender-inclusive engineering education and situating education experiences in more effective learning approaches (i.e., critical thinking in community and cultural contexts), which deserves attention in order to move engineering away from a ubiquitous view of inflexibility regarding women in engineering.</p>
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.009 |
| Scholarly communication | 0.008 | 0.004 |
| Open science | 0.000 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.014 | 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".