Information and Communication Technology (ICTs) as a Tool for Innovation
Bibliographic record
Abstract
The impact of information and communication technologies in bringing about a profound transformation in all aspects of national life in the world over does not need to be over stressed anymore. Information and communication technologies heralds a fundamental change in the dissemination of information with regard to among other things, economic, business and education. Ultimately, this paper attempts to give an overview of current initiatives around ICTs, and university development through a review of available literature. The overview will be followed by a selection of case studies of Ajayi Crowther University, documenting initiatives where ICT plays a prominent role and suggestions for further research and projects to inspire discussion for future research programme. This is part of a process to develop research programme exploring ICT innovations in, and the consequences of their possible application to university development. ICTs and university development are extensive fields, and it is not possible to address all aspects in this paper but only what has been found to be of greatest current importance. When applying ICT to university development, it has been found that the most important challenges that institutions are currently facing are the responsibilities that have been transferred to them through recent decentralization. In this paper this aspect will thus be deliberately focused upon along with the ICTs that are of most relevant to these institutions meeting their new responsibilities. Key word: Electronic resource; Information; Communication technologies; Innovation
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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 | 0.004 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.007 |
| Science and technology studies | 0.003 | 0.012 |
| Scholarly communication | 0.016 | 0.011 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.005 | 0.003 |
| Insufficient payload (model declined to judge) | 0.008 | 0.002 |
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".