Using internet things in information service in the Egyptian academic libraries: A study of Reality and planning for the future
Bibliographic record
Abstract
The Internet of Things (IoT) technology is one of the tremendous technological developments in the current era. It refers to the use of intelligently connected devices and systems to leverage data gathered by embedded sensors and actuators in machines and other physical objects. IoT is expected to spread rapidly over the coming years as it will unleash a new dimension of services that improve the quality of life and productivity of enterprises. Hence, many countries aim to benefit from these technologies in various aspects of life in general and libraries and information institutions in particular. So, the current study sheds light on the ways of benefitting from (IoT) in developing and upgrading the services of Egyptian academic libraries and how to transform them into smart information institutions. It also identifies the readiness of libraries to adopt and apply (IoT) technologies. In addition, the study measures the readiness of libraries librarians to adopt the concept of smart libraries. In this regard, the study adopted the descriptive and analytical approach, using a set of tools, the most important of which are the questionnaire and the checklist. The study population included some of the Egyptian academic libraries in government universities representing the sectors of Greater Cairo, Delta, Central and Northern Upper Egypt, in addition to some libraries of private foreign universities at Egypt, namely (German University Library - British University Library - Canadian University Library).
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.005 | 0.003 |
| Scholarly communication | 0.013 | 0.008 |
| Open science | 0.001 | 0.003 |
| 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".