Effects of course selection flexibility on academic and social opportunity structures
Bibliographic record
Abstract
Undergraduate students’ social connections influence their academic performance (Vargas et al., 2018), persistence (Zwolak et al., 2017), and mental well-being (Poole et al., 2023). Thus, it is important to consider university features that facilitate or constrain peer relationships from forming, such as curriculum. Students in majors with rigid curricula frequently co-enroll in courses with same-major peers, increasing the likelihood of forming meaningful relationships. In contrast, students in flexible majors co-enroll less often. Beyond this social role, the range of courses students take determines the disciplinary foundations to which they are exposed. We investigate how curricular flexibility influences students’ academic opportunity structures (the content students’ access through coursework) and social opportunity structures (the peer relationships students have the opportunity to form). For example, we are interested in the academic diversity of peers students co-enroll with, and the disciplinary clustering of electives. We analyzed enrollment data for a cohort of students at a college of life sciences at a large Canadian university. This includes 2,556 students across 13 majors, 11,630 offerings of 1,303 courses they took, and 62,376 co-enrolled peers from across campus. Our investigation is comparative, focusing on differences between students in the college’s most flexible major (30% prescribed credits) and those in more rigid majors (>60% prescribed credits). The study has been approved by the institutional Research Ethics Board.
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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 | 0.002 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 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".