Pathways of Peer Influence on Major Choice
Bibliographic record
Abstract
Abstract Peers influence students’ academic decisions and outcomes. For example, several studies with strong claims to causality demonstrate that peers affect the choice of and persistence in majors. One remaining issue, however, has stymied efforts to translate this evidence into actionable interventions: the literature has not grappled adequately with the fact that in natural settings, students typically select most of their peers. The bulk of causal evidence for peer influence comes from exogenously assigned peers (e.g., roommates) because peer effects are easier to identify in such cases. However, students do not form their most important ties for the convenience of scientific inference. In order to link theory and practice, we need to understand which peers are influential. We employ longitudinal, multiplex network data on students’ choices of and persistence in their majors from 1260 students across 14 universities to identify likely causal pathways of peer influence via self-selected peers. We introduce time-reversed analysis as a novel tool for addressing some selection concerns in network influence studies. We find that peers with whom a student reports merely spending time, rather than—e.g., close friends, study partners, esteemed peers—consistently and potently influence their college major choice.
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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 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".