Developing Countries Lag Behind the US and UK in Contributing to Institutional Repository Literature
Bibliographic record
Abstract
A Review of: Bhardwaj, R. K. (2014). Institutional repository literature: A bibliometric analysis. Science &Technology Libraries, 33(2), 185-202. http://dx.doi.org/10.1080/0194262X.2014.906018 Abstract Objective – Quantify the IR literature across the world by identifying countries with relatively high concentration of articles, describing the distribution of the literature by language, author (institutional and individual), journal, and examining characteristics such as the transformative activity index, and authorship and citation patterns. Design – This exploratory study of the literature used several bibliometric research methods to describe patterns and identify highly represented articles, authors, institutions, and journals. Setting – The Library and Information Science Abstracts database. Subjects – 436 articles from 118 journals. Methods – Research articles and review papers published through December 31, 2012, were identified by searching Library and Information Science Abstracts (LISA). Citation data for the 436 articles selected was gathered from LISA and Scopus. Main Results – The 436 articles from 118 journals had publication dates from 2001 through 2012, originated from 68 countries in 19 languages, and had authors affiliated with 159 institutions. The greatest number of institutional repository articles were published in 2011 while year-to-year growth was greatest from 2005-2006. Most highly represented were the United States and the United Kingdom, followed by India, Australia, and Spain. Twenty publishers were responsible for nearly half of the selected articles. The top four journals included OCLC Systems & Services, D-Lib Magazine, Serials Review, and Library Hi Tech. D-Lib Magazine alone published seven of the top 20 most cited articles. While most articles were written by a single author, the majority of the multiple author articles came from developed countries. Citation analysis reveals that the 436 articles were cited 2,071 times, for an average of 4.8 citations per article. However, 147 articles received no citations. The five most prolific authors were Elizabeth Yakel, Kim Jihyun, Karen Markey, Jingfeng Xia, and Sarika Sawant. Conclusion – The author concludes that developing countries lag behind in establishing and publishing on institutional repositories and suggests that more authors will deposit in IR in the future. A proposed role for LIS professionals is to communicate the objectives, values, and principles behind institutional repositories.
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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 | 0.010 | 0.038 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.017 | 0.067 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.020 | 0.011 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.022 | 0.006 |
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".