Proceedings of the annual meetings of the association for information science and technology: analysis of two decades of published research
Bibliographic record
Abstract
Purpose The purpose of this study is to investigate the research and publication trends in the articles published in the conference proceedings of the Association for Information Science & Technology (ASIS&T) since the year 2000. Design/methodology/approach We analyzed two decades of ASIS&T proceedings to uncover bibliometric patterns. This study uses two bibliometric procedures applied to the publications in the ASIS&T conference proceedings – a bibliometrics analysis using three data sources (Scopus, ASIS&T proceedings website and Scimago journal ranking) and a scientific mapping analysis using VOSViewer. Findings We found 3,129 publications from 2000 to 2020, with more than three-quarters jointly authored. Most authors are from the United States, Canada and China. Social media and information behavior are the top-researched areas. The top-cited journals are the Journal of the Association for Information Science & Technology , Information Processing and Management and Library and Information Science Research . Research limitations/implications The study will help information professionals understand patterns in recent research, which should help guide them in their future research directions. Practical implications The findings affirm ASIS&T’s move to an international association and point to the growing importance of collaborative work and social media. Originality/value ASIS&T has been holding annual meetings since the 1950s. While there have been various bibliometric studies analyzing publication trends in different journals in the field of information science, none of these studies have analyzed the ASIS&T conference proceedings.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.006 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".