Making Informed Decisions: EXPLORE Engineering Design Program
Bibliographic record
Abstract
Decisions must be made at the age of 16and 17 that can have long-lasting effects. High schoolstudents are asked to select a specific degree, auniversity, and sometimes even a specific discipline withvery little basis for making the decision. The EXPLOREprogram was piloted at Dalhousie University in theSummer of 2014 and 2015 to help girls in high schoolmake an informed decision about whether or not topursue an engineering degree.10 students signed up each summer to EXPLOREengineering design in a compressed 2-week schedulewhere they participated in 3 short design projects,culminating in a major project for a client from thecommunity. The girls developed documentation,presentation, leadership, and teamwork skills. Theylearned CAD software, practiced 3-D printing, and wereexposed to robotic programming. They built and tested adesign for a community partner and defended the designto a room of people. The students were introduced tovisualization techniques, the engineering design process,log books, and other essential components that theywould only otherwise encounter during their first year inan engineering program. This paper will document theelements of the course that help the girls make aninformed decision about whether or not to pursueengineering from two perspectives: the instructors' andthe student's.
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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 | 0.006 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.079 | 0.014 |
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".