Transforming Engineering Education Through Social Capital in Response to Hidden Curriculum
Bibliographic record
Abstract
This research-to-practice chapter targets prospective and current engineering educators, scholars, and leaders who are interested in learning how hidden curriculum (HC) in engineering can be transformed through social capital. HC represents the unacknowledged and often-hidden lessons or messages that hinder individuals, especially from marginalized populations, from successfully navigating their environments. HC propagates through social networks and relationships, resulting in patterns of behavior that guide how individuals navigate the structures and systems in which they are embedded. This chapter begins with an overview of HC research, discusses the connection to social capital, and introduces an HC pathways model in engineering. We introduce three HC archetypes to describe engineering stakeholders: seekers, bridgers , and agents . Seekers become aware of HC and use social capital to navigate it, bridgers surround themselves with kindred peers to support each other, and agents enact strategies and practices to challenge systems and structures. We provide example of a curriculum that aligns with these archetypes and have specific recommendations based on the US and Canada contexts for different stakeholders in engineering education.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".