A case analysis of factors affecting the adoption of grid technology by universities
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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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 | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".