Research Publications in Library and Information Science (LIS) in Nigeria: Publishing to the Wrong Audience?
Bibliographic record
Abstract
Library and Information Science (LIS) research in Nigeria span over five decades. However, despite these decades of LIS research and education in Nigeria, problems continue to plague the development of the profession in Nigeria. This paper is a comparative study of Nigerian LIS researched articles published in foreign and local LIS journals. An analysis of LIS researched articles, as contained in the online version of Library and Information Science Abstracts (LISA), Library Literature (LL) and Information Science Abstracts (ISA), from 1968 to June 2002 was carried out in order to compare LIS research publications about Nigeria by Nigerian authors that were published in foreign and Nigerian journals. The results show that a majority of the authors published in foreign LIS journals since the early 1980s till date. Owing to the difficulty encountered in accessing foreign journals in Nigeria, publishing Nigerian LIS research in foreign journals deprives the nation of the benefits of utilising these research results for the solution of LIS problems in Nigeria. The practice of Nigerian researchers publishing in foreign journal titles is examined and the problems associated with local access to these foreign journals and its implications on the growth of the LIS profession in Nigeria are highlighted. Suggestions are provided to encourage local publishing of LIS research in Nigeria. Af. J. Lib. Arch. & Inf. Sc. (2003) 13, 93-106
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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 | 0.008 | 0.045 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.006 | 0.014 |
| Science and technology studies | 0.005 | 0.005 |
| Scholarly communication | 0.019 | 0.012 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".