Gender, Social Background, and the Choice of College Major in a Liberal Arts Context
Bibliographic record
Abstract
Enduring disparities in choice of college major constitute one of the most significant forms of gender inequality among undergraduate students. The existing literature generally equates major choice with career choice and overlooks possible variation across student populations. This is a significant limitation because gender differences in major choice among liberal arts students, who attend college less for specific career training and more for broader learning objectives, are just as great as among those choosing pre-professional majors. This study addresses this gap by examining how privileged men and women at an elite, liberal arts university select their fields of study. Drawing on in-depth interviews, findings contradict the prevailing assumption of a unitary model of major choice as career choice by revealing a plurality of gendered meanings around choosing a field of study. Majors may play an important part in the construction of an intellectual identity as much as a means of career preparation. How students approach the choice relates to both gender and social background. For privileged students, traditional gendered associations with bodies of knowledge hold salience in their decision making as well as expectations of reproducing future elite family roles. This research also illuminates how gendered processes of choosing fields of study take place in relationship to particular institutional contexts.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".