THE ‘JOURNAL OF THE AMERICAN SOCIETY FOR INFORMATION SCIENCE AND TECHNOLOGY’ (JASIST): \nA SCIENTOMETRIC ANALYSIS
Bibliographic record
Abstract
This paper presents a quantitative study of source items published in JASIST Journal. It mainly focuses on growth of source items; the citation and cited references cited; domain wise distribution of source items; authorship and collaborative pattern; prominent authors based on h-index; geographical distribution; and assessment of successful papers based on specific indicator. A total of 2224 source items published in 255 issues of 12 volumes of JASIST journal. There is an upward steady growth in number of source items, times cited and cited references during 2001-2012. Information Technology and Library technology; Information source support channels; Users literacy and reading; Information treatment and Information services are the macro and micro domain fields of Information Science and Technology. Article; Book Review; Editorial Material; Letter; Review; article proceeding papers are the major document type of the JASIST publication. Most source items i.e. 971; 610; and 360 of source papers respectively have come with single, second and third authorship pattern. The most number of papers have contributed from USA (996); UK (226) and Canada (124). The country wise source items impact factor, h-index have been measured based on local citations, USA (14 h-index); UK (11 h-index) and Canada and Netherland each having 8 h-index. There are prominent authors, have been identified based on the higher value of h-index. According to this Leydesdorff L and Spink A both were considered top rank with having 8 h-index each. This paper also depicts successful papers measured by its number of citations i.e. time cited.
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 | 0.012 | 0.050 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.019 | 0.040 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.014 | 0.006 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.022 | 0.009 |
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".