Examining the “narrow” and “expansive” socio-technical imaginaries influencing college students’ collaborative reasoning about a design scenario
Bibliographic record
Abstract
Abstract Many studies show that college engineering students' sense of ethical and social responsibility declines over the course of their college careers (Cech, 2014; Canny & Bielefeldt, 2015; Schiff et al., 2021). One reason is that many college engineering programs and courses reinforce the social-technical dualism, which treats social and macro-ethical issues as distinct from the technical work more often associated with "real" engineering. Some programs, like the Science, Technology and Society (STS) program at [institution made confidential for review], attempt to challenge this dualism by supporting the integration of social and technical considerations within students' design work and by asking students to grapple with the complex ethics of their work. However, this program is still embedded within a department, university, and society that subscribes to harmful ideologies such as technocracy, capitalism, and meritocracy, which value efficiency, surveillance, and control. These ideologies and their associated values constrain the imagination for what is possible in design work, for instance, by relying on technological 'quick fixes' to address complex social problems or by propping up large corporations as innovators, without adequately grappling with the harm that these corporations might be doing. This cultural reality creates an uphill battle for educators attempting to support engineering students' sense of social consciousness and ethical responsibility. Thus, this study attempts to understand how engineering students' imaginations are being constrained by societal structures and ideologies and when do they "break free" from these constraints? In this paper, we present a preliminary analysis of first-year STS students collaboratively reasoning through a simulated design scenario about a small community store facing challenges related to the Covid-19 pandemic (adapted from Gupta, 2017). Using discourse and narrative analysis, we analyzed multiple focus group interviews to identify what we call "co-occurrences," or ideas that tend to hang together in participants' reasoning. Examining these co-occurrences provides insight into the variety of ways socio-technical imaginaries play out in students' design thinking.
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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 | 0.001 | 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 | 0.000 | 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".