Zambia Journal of Library and Information Science
Bibliographic record
Abstract
Information literacy (IL) is central to student success, it provides lifelong skills that can support their academic career.This study aimed to assess the IL skills of undergraduate students at the University of Zambia (UNZA) by applying the American College of Research Libraries (ACRL) Framework.Specific objectives of the paper were to examine how students' IL skills align with the Framework's six Frames and their accompanying knowledge practices and dispositions and to explore the feasibility of adopting the Framework for Information Literacy (IL) instruction at UNZA.An exploratory qualitative methodology was used, with 22 purposively selected participants placed in four focus groups.Discussions were audiorecorded, transcribed, and analyzed using NVivo 12. Participants demonstrated some, but not all, knowledge practices and dispositions across the Framework's six concepts.They understood key IL attributes such as assessing source credibility using authority measures, citing sources of information, and interacting frequently with peers.Furthermore, it was clear that participants used the Internet to complete their assignments.Despite being aware of the wealth of information and credible resources in databases, they lacked guidance on how to search and access them.Additionally, they indicated that they had received no IL training and had limited awareness of the librarian's role.Knowing that UNZA students already possess some information literacy skills, it is feasible to build on their existing prior IL knowledge to integrate the Framework concepts into any library instruction.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.017 | 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".