USING INDIVIDUAL-BASED MODELING TO BETTER UNDERSTAND THE HIDDEN CURRICULUM OF ENGINEERING
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Engineering culture perpetuates norms that are unwelcoming to minoritized identities, particularlywomen and racialized folks. A theory useful for understanding this is “hidden curriculum” whichdescribes the assumptions and beliefs that are unintentionally and implicitly taught in engineeringeducation. This paper outlines an initial conceptual model for using IBM (individual-based modeling) to better understand the hidden curriculum of engineering. We provide an overview of the driving question behind the model design, the agents and their attributes, the rules andprocesses which change these attributes, and the scale of the model. This overview of the model building process provides insight into the model design for simulating and better understanding the perpetuation of the hidden curriculum within 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.
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 | 0.002 |
| 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 | 0.000 | 0.000 |
| 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 it