What Your PhD Advisors Can’t Tell You Because They Don’t Know: Landing a Job at a Student-Focused Institution
Bibliographic record
Abstract
More than half of political science professors in the United States are employed in non-PhD granting departments.1 While some of these are research intensive (R1 or R2) institutions, many more are institutions where undergraduate education is the primary focus (hereafter “student-focused” institutions). For faculty members who prioritize teaching and want to closely mentor undergraduates, student-focused institutions provide meaningful and rewarding careers. At the same time, most PhD faculty have spent their graduate and professional careers in departments with doctoral programs at R1s and R2s. They may not provide very good advice for applying and interviewing for jobs at student-focused institutions because they have never worked at such an institution. I spent a quarter century at a regional, master’s institution in the South, where the regular teaching load was eight courses per year. I spent 20 years in administration, which included 16 years as department chair and dean. I participated in scores of searches and many issues came up repeatedly. This essay will provide concrete advice on how to prepare an application and interview at a student-focused institution from someone on the other side of the interview desk.
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 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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.002 | 0.003 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.007 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.005 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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; both teacher heads agree on what is shown here.
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".