The Reasons of Job Alienation among the Faculty Members of Hebron & Al-Quds Universities
Bibliographic record
Abstract
The present study aimes to identify the reasons behind experiencing job alienation by the faculty members at Hebron& Al-Quds universities. It was conducted during the second semester of the academic year (2017/2018). The study’spopulation involves all the faculty members who work at Hebron & Al-Quds universities (i.e. 446 faculty members).The study’s sample consists of (200) faculty members. Those members were selected through using the randomstratified sampling method. The study’s instrument was developed by the researchers, and It is represented in aquestionnaire. The questionnaire seeks to identify the reasons behind experiencing job alienation by the facultymembers at Hebron & Al-Quds universities. It consists of (20) statements. These statements address two types ofreasons; a)-reasons that are related to the university management & b)- reasons that are related to the workcolleagues. The researchers checked the validity of questionnaire, and they measured the reliability of thequestionnaire through using the relevant statistical methods. It was found that the statements that concern theexamined reasons show moderate means. In addition, it was found that there is not any statistically significantdifference between the respondents’ attitudes which can be attributed to the university.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".