The Trend of the Bibliographical Output from Libyan Engineering Schools: A 30-Year Review From 1984-2013
Bibliographic record
Abstract
In this study, the research output from the major Libyan engineering schools was gathered and compared for the period of thirty years (from 1984 to 2013). The Elsevier database, Science Direct, was used to gather these publications and only engineering articles were included. A comparative analysis was performed on three levels; first a local comparison between the different faculties of engineering across Libya and secondly, a broader comparison between Libya and the neighboring METAL (Morocco, Egypt, Tunisia, Algeria and Libya) countries and finally, the third comparison was performed between Turkey and the METAL countries. In the local comparison, the output was normalized by the number of teaching staff while in the broader regional comparison, gross domestic product and population were used as standardization factors. When analyzing the research output of the Libyan engineering schools, it was observed that most publications came from Tripoli (47.1%, n=131) followed by Benghazi (25.9%, n=72), Misurata (4.1%, n=12) and Omar Al-Mukhtar (4.0%, n=11). However, when the number of staff members was taken into consideration, Benghazi University and Omar Al-Mukhtar University had higher research productivity levels than Tripoli University and Misurata University respectively. The regional comparison showed a clear difference between Libya and its neighbors, having the lowest output among them. Finally, it was found that across the three decades under study, Turkey produced more research than all the METAL countries combined. More attention needs to be paid to research and publications in Libyan engineering schools. A number of recommendations were made to help improve the publication rate in Libyan engineering faculties
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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 | 0.008 | 0.026 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.078 | 0.117 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".