Pathways of Peer Influence on Major Choice
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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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Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it