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Record W7018631073

Effects of course selection flexibility on academic and social opportunity structures

2025· article· en· W7018631073 on OpenAlexaboutno aff

Bibliographic record

VenueScholarship@Western (Western University) · 2025
Typearticle
Languageen
FieldSocial Sciences
TopicHigher Education Research Studies
Canadian institutionsnot available
Fundersnot available
KeywordsFlexibility (engineering)DisciplineDiversity (politics)CurriculumSelection (genetic algorithm)Higher educationLife course approachStudent life
DOInot available

Abstract

fetched live from OpenAlex

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.

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.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.005
Threshold uncertainty score0.722

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.001
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.117
GPT teacher head0.439
Teacher spread0.323 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2025
Admission routes1
Has abstractyes

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