Application of ICTs in collection development in private university libraries in Kenya
Bibliographic record
Abstract
Purpose The purpose of this study was to find out ways in which collection development practices in private university libraries in Kenya could be enhanced by the use of information and communication technologies (ICTs). Design/methodology/approach This paper is based on research that was carried out on the application of ICTs in collection development in selected private universities in Kenya. The study was done using a survey method. Three universities and a total of 72 respondents were purposively selected for the study. The respondents included librarians, faculty deans and postgraduate students from the three universities. Findings The findings indicated that ICTs were available in all the three selected universities but their application in collection development was not adequate in ensuring efficiency and in making sure that the library collections are effective in meeting the needs of the users. Originality/value With the changing information environment and users' information needs, libraries are being compelled to adopt ICTs in order to remain relevant and increase their value and meet the changing needs of the users. The paper recommends different ways of applying ICTs in all the processes of collection development to make the process more efficient and effective in meeting the needs of the users.
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 | 0.008 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.007 | 0.002 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".