Financial Incentives for Adopting Cloud Computing in Higher Educational Institutions
Bibliographic record
Abstract
Teaching today relies a great deal on IT resources which require large investments and there are many higher institutions that cannot afford such investments. Educational institutions usually search for opportunities to better manage their resources, especially after the economic crisis, which has resulted in reducing government support, especially in western countries. It is argued that ‘cloud computing’ is one of those opportunities for any educational institution due to its benefits in terms of cost reduction. Today, ‘cloud computing’ can be seen as one of the latest dynamic services in the IT world because of its flexibility. This paper investigates the financial incentives for adopting cloud computing in higher educational institutions. To achieve this objective the research employs a qualitative method to collect the data. Interviews were conducted with a number of cloud service providers, experts in the field and users/potential users of the cloud. The results reveal that cloud computing drives down up-front and on-going costs, and that the number of IT staff can be reduced if the cloud is adopted. Disaster recovery and business continuity are other cost-savings areas for an educational institute in adopting the cloud, and cloud computing provides low cost testing and a development environment solution.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.012 | 0.039 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".