Scientometric Analysis of Library Science Literature Identifies Publication Trends and Thematic Developments Concerning Equity, Diversity, Inclusion, and Accessibility (EDIA) in Research Published Since 1971
Bibliographic record
Abstract
A Review of: Ashiq, M., Ur Rehman, S. & Warraich, N.F. (2023). A scientometrics analysis of equity, diversity, inclusion, and accessibility (EDIA) literature in library and information science profession, Global Knowledge, Memory and Communication. Advance online publication. https://doi.org/10.1108/GKMC-12-2022-0298 Objective – To analyze publication trends, authorship and collaboration patterns, and thematic developments concerning equity, diversity, inclusion, and accessibility (EDIA) in library and information science (LIS) research. Design – Scientometric analysis. Setting – The indexing and citation platform Web of Science (WoS), accessed on July 14, 2022. Subjects – A collection of 628 articles, proceeding papers, and reviews published between 1971 and 2021. Methods – A literature search of citations assigned under the WoS subject category of “Information Science Library Science” was conducted using the keywords EDIA, LIS, and synonyms of LIS in the WoS “Topic” field. The authors then each manually reviewed title and abstract information of the resulting citations for relevancy. Finally, bibliometric analysis was conducted on the selected 628 articles using MS Excel, VOS viewer, Biblioshiny, and CiteSpace to discern publication patterns, characteristics, and relationships between the remaining publications. Main Results – The researchers identified a period of rapid growth in both publications and citations of LIS EDIA literature between 2006 and 2010. Although while publications of EDIA research continued to slowly increase each year, annual totals of cited EDIA publications and average citations per article have begun to gradually decline since 2015. Using similar publication and citation metrics as indicators for impact, the United Kingdom was identified as host to the most (n = 5) top-10 influential LIS journals for EDIA scholarship over the 50-year study period, while the United States was home to the most productive LIS authors and institutions for EDIA research, and the leading country in publications, citations, and citation impact measures. The researchers further identified five interconnected EDIA thematic streams using co-citation analysis of the 150 most cited articles, including, in descending order of stream size: disability and accessibility; diversity, inclusion, and recruitment; social justice and libraries; libraries and immigrants; and libraries and the lesbian, gay, bisexual, transgender, and queer (LGBTQ) community. Conclusion – The authors recommended further investigations and increased support for EDIA issues by LIS researchers and policymakers, especially for the smaller or less matured subject streams and in underrepresented geographic regions. Future researchers are also encouraged to conduct similar bibliometric analysis using other LIS databases.
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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 | 0.050 | 0.118 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.204 | 0.254 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.009 | 0.009 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".