Making EDI Visible: The Power of Identity Theories in Reviewing the Engineering Career Paths Literature
Bibliographic record
Abstract
In this paper, we present a literature review exploring the intersections of engineering professional identity, engineering career paths, and equity, diversity, and inclusion (EDI). In particular, we analyze the engineering career paths literature through the lens of 3 identity theories: role identity theory, social identity theory, and social constructionism. In the end, we found that authors who backgrounded EDI were most likely to highlight traditional roles and individual-level traits as career advancement indicators, whereas authors who explicitly considered EDI acknowledged the impact of social structures and norms on identity formation and career paths. With an understanding that both social and professional identity can impact engineers’ career paths, we argue for the importance of foregrounding EDI in the study of engineering identity and career paths, and recommend future work that deliberately investigates the intersection of these concepts.
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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 | 0.037 | 0.066 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.021 | 0.016 |
| Science and technology studies | 0.006 | 0.016 |
| Scholarly communication | 0.013 | 0.024 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.003 | 0.007 |
| Insufficient payload (model declined to judge) | 0.002 | 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".