Work experience and satisfaction of male and female professors in Turkey: signs of progress?
Bibliographic record
Abstract
Purpose This research aims to investigate gender differences among professors in Turkey. Design/methodology/approach Data were collected from 243 male and 165 female professors using an anonymously completed internet based questionnaire. Measures included personal demographic and work situation characteristics, workaholism components and validating job behaviors (e.g. perfectionism) workaholism antecedents (e.g. beliefs and fears, organizational values), work and extra‐work satisfactions, and psychological well‐being. Findings There were considerable differences in personal demographic and work situation characteristics. Female professor were younger, more likely to be single, more likely to have fewer children, had less job and university tenure, were at lower ranks and earned less income. Female and male professors were similar on workaholic behaviors, work and extra‐work satisfaction and psychological well‐being, with one exceptions: female professors reported more psychosomatic symptoms. Practical implications Despite considerable demographic and work situation differences, female and male professors in Turkey report similar job behaviors, satisfactions and psychological well‐being. Originality/value Provides information on personal demographic and work situation characteristics among male and female academic staff in Turkey.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".