Academic Rank and Position Effect on Academic Research Output – A Case Study of Ariel University
Bibliographic record
Abstract
The aim of this study is to explore the effects of professional factors (academic rank and academic-administrative role) and home-unit-related factors (affiliation and number of faculty members in the faculty) on faculty members’ research output, measured by number of citations. Research literature on operations research in the academia reflects a dual approach to the association between number of citations and research quality, although it is generally concurred that the number of citations is taken into consideration in assessments for promotion and tenure, and represents a measure of publication quality. The association between faculty members’ administrative roles and their academic output is explored for the first time in this study.We collected data on four citation-related variables for 315 senior faculty members, as well as their affiliation, academic rank, and administrative/academic role, if any. Structural Equation Modeling (SEM) was employed to test the model’s goodness of fit.Findings show that faculty affiliation, academic rank, and academic-administrative role affect number of citations. The association between number of citations per faculty, engagement in administrative tasks, and the number of faculty members in the faculty has significant implications for faculty promotion policies and the “price” faculty members pay for assuming administrative duties, especially in the early years of their academic career. Furthermore, the faculty also plays an important role in academic outputs, and its organizational climate may promote or disrupt research-oriented academic careers.
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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 | 0.005 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".