Collaboration clusters, interdisciplinarity, scope and subject classification of library and information science research from Africa: An analysis of Web of Science publications from 1996 to 2015
Bibliographic record
Abstract
This study investigated the trends in the scope and subject classifications of library and information science research from authors that are affiliated with institutions in Africa. Library and information science journal articles and conference proceedings from the 54 African countries that were published between 2006 and 2015 and indexed in the Web of Science were retrieved for the study. After the removal of non-relevant articles and articles that were not available online, the library and information science publications were classified based on subject and scope. Results from the analysis of author keywords, country of affiliation, subject and scope classification were also visualized in network maps and bar charts. Frequency analysis shows that though computer science had the most profound influence on Africa’s library and information science research, its influence came to prominence in 2004. Furthermore, North African countries exhibited features that are different from the rest of Africa; they contributed most on core computer classifications while other African countries focused more on the social science-related aspects of library and information science. Unlike other regions in Africa, the North African countries also formed a dense collaboration cluster with strong interests in subjects that are conceptual and global in scope. The collaboration clustering analysis revealed an influence of some colonial languages of as a basis for forging strong collaboration between African and non-African countries. On the other hand, African countries tend to collaborate more with countries in their regions. Lastly, human computer interaction and library and information science history subject classifications were almost nonexistent. It is recommended that further studies should investigate why certain subject classifications are not well represented.
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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 | 0.002 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.031 | 0.042 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".