A case analysis of factors affecting the adoption of grid technology by universities
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Grid computing is emerging as the foundation upon which virtual collaborations can be built among large organizations with the aim of integrating and sharing computer resources, and thus offering performance speed and resource availability, which is unattainable by any single institutional technology resources. With the level of increase in the number of tertiary institutions in Africa, and the attendant lack of basic information technology resources, the use of grid computing for collaboration purposes would contribute to the enhancement of research, course delivery, course management, and other aspects of institutional development. This paper carries out an empirical study of the possibility of adoption of grid computing as a vehicle for collaboration among tertiary institutions in Nigeria from the perspective of the potential adopters (users) of the systems. This study uncovers challenges to the adoption of grid technology by the tertiary institutions. The key challenges that significantly affect the adoption of grid computing in tertiary institutions are mainly attitudinal (perceived need and perceived benefits). Infrastructural issues (facilitating conditions) also impose limitations on the ability of universities to implement grid computing.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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 it