What Engineering Admissions Can Learn from Medical School Admissions
Bibliographic record
Abstract
Medical school admissions have undergone a drastic change in the past decade with increasing awareness about issues of professionalism and a lack of diversity among physicians. These problems are also arising among engineers, where issues of communication, ethics, and cultural competency are beginning to be identified across the profession. Over the years, the traditional admissions process into medical school has been identified to be particularly problematic, as it places a strong emphasis on the cognitive competencies of incoming students, but frequently neglects their non-cognitive competencies or professionalism. The same parallel can be drawn for admission into engineering programs, where the process strongly focuses on cognitive abilities (GPA, standardized test scores) and less so on the non-cognitive skills. In this paper, we suggest various tools to promote the shift to the assessment of professionalism in engineering admissions to ensure that the profession will keep up with the future needs of the population.
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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 | 0.011 | 0.173 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.006 | 0.005 |
| Scholarly communication | 0.018 | 0.018 |
| Open science | 0.003 | 0.011 |
| Research integrity | 0.005 | 0.006 |
| Insufficient payload (model declined to judge) | 0.025 | 0.005 |
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".