Internet Connectivity and Accessibility in University Libraries: A Study of Access, Use and Problems among Faculty of Natural Sciences Students, University of Jos, Nigeria
Bibliographic record
Abstract
Abstract Objective – This study has the objective of establishing whether the undergraduate students of the Faculty of Natural Sciences, University of Jos, have access to and use Internet facilities in the University library. Methods – A survey research design was adopted for this study and questionnaires were used in gathering data. Statistical methods used in the analysis include percentages, frequencies, and Chi-Square test for measuring the association of library visit and use of the Internet. Results – The analysis of the data and findings indicated that there is Internet connectivity in the library. The findings also revealed that few students (15.5%) use the computer and the Internet on a daily basis. The problems of slow Internet connection at peak periods and unsteady power supply were clearly identified. Furthermore, the analysis revealed that there is no association between the students’ library visits and their use of the Internet for most academic purposes, except for downloading articles. Conclusion – The presence of Internet connectivity in the library, does not translate to meaningful academic behaviour among the students. Therefore, sensitising and training of the students on Internet usage were recommended for better academic performance and life-long learning.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".