A Bibliometric Study of Papers Published in Library and Information Science Research during 1994 2020
Bibliographic record
Abstract
The paper analysed 699 papers published in Library & Information Science Research (LISR) during the period of 1994-2020. Google Scholar was used to obtain the number of citations received by these papers until April 30, 2021. The study examined the geographical distribution of published articles and also identified prolific institutions and authors. The study examined the impact of output of countries, institutions and authors using citation per paper (CPP) and i-10 index as indicators of impact. The study also examined the pattern of growth and identified the highly cited papers. Based on the analysis of data it is observed that maximum articles were published during the three years block of 2015-2017. The geographical distribution of output indicates that 51 countries contributed the 699 papers. Highest number of papers was contributed by authors from the USA though it had a low value of CPP in comparison to Norway and Finland. Among the institutions, Florida State University (USA) topped the list. However, University of Illinois at Urbana-Champaign, USA had the highest value of CPP. During the period of study, 1,389 papers received 74,061 citations, of which only 41 (3 %) articles remained uncited.
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How this classification was reachedexpand
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | Bibliometrics Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Observational | low |
| gpt | Bibliometrics Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Other design | high |
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 | 0.005 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.083 | 0.154 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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, unvalidatedLabeled directly by 2 models reading the full record.
The models disagree on parts of this classification; every voice is preserved in the section at the end of the page.
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".