Flexibility at the Core: What Determines Employment of Part-Time Faculty in Academia
Bibliographic record
Abstract
In this study, we examine institutional predictors of part-time faculty employment in the higher education sector in the United States. We draw upon institutional and individual-level data to examine the variation in the intensity of part-time employment in faculty positions among a representative sample of higher education institutions. Institutional-level data are from Integrated Postsecondary Education Data System (IPEDS) and individual-level data are from National Study of Postsecondary Faculty (NSOPF). These data allow us to examine the impact of both economic factors and social environment on employment practices of colleges and universities. This analysis adds to the emerging literature on non-standard work arrangements in core organizational functions. Our results suggest that the employment of part-time faculty is significantly associated with a set of organizational attributes and characteristics such as institutional type, sources of revenue, and part-time student enrolment. Private institutions, on average, have higher levels of part-time faculty than their public counterparts. The proportion of part-time students and the share of institutional revenues derived from tuition and fees are positively associated with part-time faculty employment. Faculty unions are positively related to the employment of part-time faculty. Finally, institutions that have limited resource slack and pay high salaries to their full-time faculty members tend to employ a high proportion of part-time faculty. These results support the arguments that higher educational institutions actively design and adopt contingent work arrangements to manage their resource dependence with constituencies and to reduce labour costs.
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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 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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; 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".