The Relationship between School/Department Rankings, Student Achievements, and Student Experiences: The Case of Psychology
Bibliographic record
Abstract
What predicts academic success during graduate school? What are the experiences of graduate students in terms of happiness, stress level, relationships in the program, and feelings of autonomy/competence? Responses from 3,311 graduate students from all psychological disciplines in the US and Canada were collected to answer questions involving (1) the relationship between student-level variables and department/school rankings (US News & World Report, Carnegie Foundation, National Research Council), (2) the determinants of important student-level variables such as number of publications, posters, and life satisfaction, and (3) examining the variables year-by-year in the program to explain changes over time at different points in the graduate career. Results reveal the degree to which certain aspects of higher ranked departments/schools impact student achievements such as number of publications and teaching experience. The results also reveal a unique year-by-year progression including a consistent decrease of happiness for every year in graduate school. While the findings were collected in psychology, the answers to these questions may resonate with graduate students across disciplines that are experiencing similar forces that characterize the graduate school experience. The results can also inform current conversations about the direction of higher education and the value of the graduate school experience.
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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 | 0.004 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".