Achievements in Research and Teaching—Investigating the Effect of Age and Gender
Bibliographic record
Abstract
Purpose. This study deals with the research and teaching achievements of faculty members as affected by demographics. The topic of age of employment, as well as age of retirement, is one that occupies modern society, both in research and with regard to the significance of age for the labor world in practice. Gender-related differences regarding this issue have occupied the academic literature as well. In the current study we examined the impact of age and gender on research output (by number of citations) and satisfaction with teaching (by student survey scores). Method. Empirical data on article citations and teaching surveys were gathered for 315 senior faculty members at Ariel University, Israel. Structural equation modeling was used to test the model’s goodness-of-fit. Findings indicate that the higher the age of the faculty members the greater their output. The opposite is true of teaching surveys. Age appears to contribute to the number of article citations and less so to students’ satisfaction with the teaching of senior faculty members. A sensitivity analysis was also performed. Men were found to have a higher number of citations than women. Results and discussion. The research findings have practical meaning. The achievements of academic faculty members are undoubtedly age-dependent: seniority and experience contribute to research (number of citations) and do not contribute to teaching as measured by student satisfaction. The question is whether in the modern era, when quality of life and life expectancy are on the rise, there is room to breach the employment age limitations in academia, particularly for high academic producers, in light of their achievements.
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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 | 0.006 | 0.032 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".