A typology of undergraduate biology research experiences in Ontario
Bibliographic record
Abstract
Undergraduate research experiences (UREs) have long been integrated into the landscape of undergraduate education, and the typical, one-on-one model has been associated with several positive student outcomes. Newer models of URE, aimed at improving scalability and promoting access for larger cohorts of students, have proliferated. However, due to the absence of a systematic classification of the models of UREs, comparisons across model types are limited, particularly in Canada. Therefore, it is unclear if these scalable models have achieved the aim of providing a more accessible, but equally impactful URE. We used principal component analyses of key variables derived from the course syllabi of 76 UREs to generate a typology of curriculum-based biology UREs, categorized into the following: Type A (apprenticeship-style research), Type B (field courses), and Type C (high enrollment, course-based research). Analysis of the course characteristics of these three course types revealed that Type C courses were the best positioned to provide an accessible learning environment and to include students who would otherwise not participate in research. The development of a typology of UREs provides a foundation to extend previous research on undergraduate research courses—which primarily focuses on the apprenticeship model—to include the other course types characterized in this study.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.007 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 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 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".