Sociotechnical Thinking as a Contextualizing, Transcending, and Cross-Connecting Concept for Engineering Learning
Bibliographic record
Abstract
The complexities of contemporary engineering problems and solutions require deep attention to multiple social and technical aspects pertaining to the situation. Sociotechnical thinking in engineering offers a way to navigate these social and technical complexities that inform the way we inquire and design. While sociotechnical thinking as it relates to engineering education has been explored in various ways, sociotechnical thinking remains an abstract concept with multiple meanings and interpretations. Educators and researchers must think critically about dominant conceptualizations to appreciate how these meanings inform teaching, learning, and practice. In this paper, we describe sociotechnical thinking as a contextualizing, transcending, and cross-connecting concept for engineering learning. First, we present three assumptions that may be limiting current approaches to sociotechnical thinking. Then, we illustrate three different shifts in framing sociotechnical thinking to contribute to an integrated understanding of sociotechnical thinking as it relates to curriculum and pedagogical design. With these shifts, we aim to add meaning and richness that expands pedagogical opportunities for sociotechnical thinking in engineering curriculum, learning and practice.
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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 | 0.014 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.004 |
| Science and technology studies | 0.008 | 0.089 |
| Scholarly communication | 0.017 | 0.018 |
| Open science | 0.002 | 0.011 |
| Research integrity | 0.004 | 0.008 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".