DESIDOC Journal of Library Information Technology
Bibliographic record
Abstract
This study explores the publication trends of scholarly papers in DESIDOC Journal of Library & Information Technology from 2010 to 2024. The result showed that 851 articles were published during the study. The study examined various aspects such as the authorship pattern, year-wise distribution of publications, degree of collaboration, author productivity pattern of contribution, year-wise authorship pattern of publication, and category-wise classification of paper contribution. The study identifies the author who published the most articles during the study period. Additionally, the paper analysed the category-wise classification of paper contributions and examined the year-wise distribution of publications. The study revealed the authorship pattern during the study. The study found most articles are contributed by two authors, followed by single and more than three authors. The study also revealed the total number of papers published in the Journal from 2010 to 2024. This bibliometric study provides an in-depth analysis of the DESIDOC Journal of Library & Information Technology (DJLIT) from 2010 to 2024. The co-authored papers dominate the journal’s output, reflecting a collaborative research approach. The journal’s content has evolved to encompass a wide range of topics within LIS, including digital libraries, information retrieval systems, and knowledge management. This thematic diversification aligns with global trends and reflects the journal’s responsiveness to emerging issues. While the majority of contributions are from Indian authors, there is a noticeable increase in international collaborations, particularly from countries such as the United States, the United Kingdom, and Canada. This trend highlights the journal’s expanding global reach and relevance. The DESIDOC Journal of Library & Information Technology has demonstrated significant growth and evolution over the past 15 years.
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 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.016 | 0.023 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.021 | 0.005 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.051 | 0.021 |
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".