Equity, Diversity, and Inclusion Strategies in Engineering and Computer Science
Bibliographic record
Abstract
This article delves into the issues of equity, diversity, and inclusiveness (EDI) in the engineering disciplines in Canada and Spain and presents the challenges faced by underrepresented individuals and ways to promote an inclusive and diverse environment. Two strategic lines are identified: (a) facilitating university education access to underrepresented and minority groups and (b) guiding such students during university training to set them up for successful future careers. Accordingly, this article shows how the strategies mentioned above are implemented in some selected Canadian and Spanish universities, clearly distinguishing the approach taken in the two countries. In Canada, there is a more decentralized approach to addressing EDI issues, wherein the universities devise their agendas independently. In Spain, on the other hand, there is a stronger and more direct involvement of the government to ensure a comprehensive, system-wide approach to tackling EDI issues in academia. This article helps education policymakers to devise and implement pragmatic strategies for achieving EDI and the relevant UN-defined sustainable development goals.
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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 | 0.015 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.019 | 0.033 |
| Scholarly communication | 0.014 | 0.006 |
| Open science | 0.002 | 0.027 |
| Research integrity | 0.003 | 0.003 |
| 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".