A Comparative Study of Faculty Members’ Career Advancement (Promotion) Systems in the United States and the Islamic Republic of Iran: Case Analysis of the University of Tehran and Portland State University
Bibliographic record
Abstract
This article examines the similarities and differences in the systems for faculty career advancement in higher education institutions in the United States and the Islamic Republic of Iran. The analysis focuses on two specific cases: the University of Tehran and Portland State University. Through this paired comparison, we draw out the similarities between the two cases. Both cases are public universities and share similar criteria pertaining to productivity in research, teaching, and community outreach/service as central aspects in their respective faculty evaluation guidelines. On the other hand, we find significant differences in terms of the following parameters: the degree of centralization in the decision-making process regarding promotion and tenure, specific guidelines pertaining to the adherence to Islamic ideology in the Iranian case, which lack a comparative equivalent in the American case, institutional mechanisms for faculty representation, and the ratio of tenure-track to non-tenure track faculty. The purpose of this article is to improve universities faculty members’ career advancement (promotion) systems through the identification of effective practices in an effort to develop better models of higher education.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".