Requirements and Standards of Electronic Governance and Their Relationship to Institutional Performance in Libyan Universities: Case Study of Fezzan University
Bibliographic record
Abstract
This study is directed to detect the requirements and standards of e-governance and their association with enhancing institutional performance from the faculty members at Fezzan University, in the context of hasty transformations affecting higher education institutions. The study implemented a descriptive-analytical approach, and data were collected through an electronic questionnaire distributed to a sample of faculty members, yielding 100 valid responses for analysis.The statistical analysis showed a strong and positive statistically significant relationship between the accessibility of e-governance requirements and standards and the enhancement of institutional performance at Fezzan University. The Spearman association coefficient between the total e-governance requirements and standards and institutional performance was 0.760, with a significant level of p = .000.The outcomes indicated that the highest-rated aspects of e-governance application at Fezzan University were adherence to ethical standards for the use of digital data and transparency in issuing administrative verdicts and information. Additionally, statistically significant differences were observed in faculty members’ evaluations of e-governance standards based on age and years of experience. Furthermore, significant differences were found in evaluations of institutional act according to gender and age.The study suggests continuous updating and looking after electronic systems to meet cybersecurity requirements and data protection standards. It also emphasized the importance of reinforcing the values of transparency, accountability, participation, and fairness in university processes. Moreover, the study proposes organizing constant training programs for staff and faculty members to enhance digital skills and understanding of e-governance practices.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".