User Education Practices on Utilization of Electronic Resources at the Kenya Revenue Authority Library, Nairobi
Bibliographic record
Abstract
This paper is slightly adapted from my master’s thesis. It addresses the first objective which was to determine the types of user education practices on the utilization of electronic resources at the Kenya Revenue Authority (KRA) library. The purpose of this paper was to assess how user education practices are planned, organized and implemented at the KRA Library in response to the challenge of orientation, training and instructing users on the use of information sources and services. User education is a life-long process that has no end. While seeking information services of a library, users need education to effectively use library resources, facilities and services. This ensures that users are aware of the available resources and how to access them to support their needs. However, electronic resources in organizations may not be fully utilized as a result of inadequate user education practices. The ultimate goal of librarians and libraries is to educate users to discover their information needs, encourage and motivate them to use library resources and services. The user is the most important component in a library or information system and is the last link or receiver of information in the communication cycle. The descriptive research design was used while both quantitative and qualitative approaches were used in this study. The census technique was applied to select the sample size from the study population. The study established that the types of user education programmes practiced at the KRA library include library orientation and bibliographic instruction among others. It recommends for extension of user education programs, frequent user surveys and integration of user education with collaborative library activities.
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How this classification was reachedexpand
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 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.000 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".