A bibliometrics analysis of the journal “library and information science research” from 2008–2017
Bibliographic record
Abstract
The present study analyses the various pattern of articles in the journal “Library and Information Science Research” over a period of 10 years from 2008 to 2017. A total number of 381 research communications from the journal published in Elsevier was retrieved and examined using well-established bibliometrics indicators. The study reveals that the journal publishes a slightly equal number of articles in each year per volume/issues. The average number of authors per issue was 19.45 during the study period. The highest numbers of 163 (42.78%) articles have jointly contributed by two authors followed by single authors with 131(34.38%) articles. The degree of collaboration falls from 0.53 to 0.75. The average number of references per article is 44.50. The study further investigates that 80.31% of the total articles were published with a length less than 10 pages. The highest number of authors contributed to the journal from the United States of America with 395 (50.99%) contributors followed by Australia (66, 8.48%), Canada (45, 5.78%). The study also reveals that the highest number of research paper published in the form of “Article” than another form of literature.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.088 | 0.100 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".