Why Would You Ask Me about Engineering Culture and Belonging? Introducing Social Science Prompts into Engineering Surveys
Bibliographic record
Abstract
What happens when researchers introduce socially theorized concepts like "culture" into engineering surveys as data generation prompts?While it is common for us to use social science theories to frame our analyses, it is less common for us to ask engineering students and practitioners to make sense of them through electronically administered surveys.In this paper, we examine 1198 open-ended responses to two items on a Canadian engineering career path survey: Q65: What aspects of engineering culture make you feel like you belong? and Q66: What aspects of engineering culture cause you to question your belonging?In addition to identifying specific factors that enhanced and constrained participants' sense of belonging in the profession, we observed three distinct ways of responding to our culture prompt: engage (14%), ignore (54%), and backlash (8%).When we disaggregated these findings by an intersectional gender/race category, we found that white men were overrepresented in "backlash" responses (11%), racialized 1 men and women (76% RM, 71% RW) were overrepresented in the "ignore" responses, and racialized and white women (23% RW, 20% WW) were overrepresented in the "engage" responses.We use these findings to generate a justice-based argument for including social science prompts in engineering education research.Our position contrasts with positivist norms about minimizing response bias. [1][2]2][3][4] When we minimize the ambiguity of survey prompts, we adopt a standard set by the white, male majority, leaving dominant ideology intact.In contrast, when we integrate social science concepts into our survey, we provide an opening for the "subaltern" to speak. 5
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".