Approaching Equity, Diversity, Inclusion, and Social Justice Education as Imperative to Engineering Curricula
Bibliographic record
Abstract
It is increasingly recognized that integrating concepts of equity, diversity, and inclusion (EDI) into engineering education is critical to the students’ personal and professional development. When engineering students learn about EDI, it can improve their working relationships with teammates and illuminate the social impact of their work on the communities they serve. It is integral to incorporate EDI into the undergraduate engineering curriculum; however, there are several challenges and questions regarding the ideal method of implementation. Since 2019, the E-IDEA (Engineering Inclusivity, Diversity, and Equity Advancement) Teamwork Initiative has been developing a series of workshops focused on EDI and teamwork that are conducted directly in engineering classrooms. Using both problem-based and experiential learning approaches, these workshops teach interpersonal skills through an EDI lens, preparing students for success in diverse teams, in the workplace, and in their communities.
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 | 0.015 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.016 | 0.026 |
| Scholarly communication | 0.016 | 0.008 |
| Open science | 0.002 | 0.027 |
| Research integrity | 0.004 | 0.010 |
| Insufficient payload (model declined to judge) | 0.003 | 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".