Re-Engineering the Culture: Perspectives of the Feminine-Identifying and Queer Engineering Student
Bibliographic record
Abstract
The inclusion of queer and feminine-identifying students in science, technology, engineering, and mathematics (STEM) is an issue which expresses ample discussion, yet little action. Students belonging to these groups face barriers relating to identity of group and self, peer exclusion and intolerance, and trivialization of their work. This paper reviews the literature of 21st century United States and Canada on this topic, expanding into closely related topics, including underrepresentation, cultural cis-heteronormativity, essentialism, and marginalization. It reveals the firsthand experiences of students affected by the cultures of their engineering faculties, as well as their attempts to either persevere through the repression of identity or remain sidelined by their environment. This narrative literature review provides solutions based on research, including EDI curriculum implementation, supportive student clubs, queered survey methods and moderated language. These solutions offer direction for future research efforts and provide methods on scales small or large to improve inclusion in engineering education.
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 | 0.011 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.034 | 0.056 |
| Scholarly communication | 0.016 | 0.008 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.003 | 0.010 |
| 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".