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Record W1590900504 · doi:10.6846/tku.2009.00643

西文資訊科學期刊文獻之引用分析研究:以JASIS(T)為例

2009· article· zh· W1590900504 on OpenAlexaboutno aff
方碧玲

Bibliographic record

Venuenot available
Typearticle
Languagezh
FieldMedicine
TopicDiverse Approaches in Healthcare and Education Studies
Canadian institutionsnot available
Fundersnot available
KeywordsSubject (documents)CitationLibrary scienceZipf's lawBibliometricsCitation analysisInformation retrievalComputer scienceMathematicsStatistics

Abstract

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By examining the references of research and specific topic articles of JASIST, this study explored the disciplines and subjects relating to information science. According to the website of JASIST, there are 1,341 research and specific topic articles with 51,359 references during the period of 1998 to 2008. Since journal articles and monographs are cited by JASIST most, the citation analysis of this study will focus on these two document type references only. Firstly, this study applies the Bradford's Law and Bradford-Zipf's Law to identify the core journals which were cited by JASIST. Then, search the classification number and subject categories of WorldCat and Ulrichsweb.com and also the descriptors of LISA to analyze cited references. Followings are the research results: 一、JASIS(T) published 2,031 articles from 1998 to 2008, with an average number of 185 per year. Among them, 1,341articles are research and specific topic paper, contributing 66.02% of all published items. Journal articles and monographs are two most cited reference for research and specific topic articles. 二、JASIS(T) has cited a total of 27,115 journal literature, distributing over 2,994 journals. By applying the Bradford's Law, there are four core journals cited by JASIS(T). However, by applying the Bradford-Zipf's Law, there appears to be ten core journals. The journal that had been cited the most is JASIS(T) itself (17.47% of all citations), suggesting JASIS(T)'s self-citation is obvious. 三、For journals cited by the JASIS(T), bibliography. library science. information resources (general), science, and social sciences are the three most cited disciplines. The most commonly cited subjects, which identified from WorldCat and Ulrichsweb.com, are information science, information technology, information storage and retrieval systems, library science, and science. However, searching, online information retrieval, information work, subject indexing, and information storage and retrieval, which searched from LISA, are the most cited descriptors of the library and information science journals cited by JASIS(T). 四、JASIS(T) has cited a total of 27,115 monograph literature, distributing over 5,565 books. Introduction to Modern Information Retrieval written by Salton & McGill was cited the most. Three of the top ten cited monographs are written by Salton. It may suggest that Salton is one of the most influential authors in the field of information science. In addition, 91% of the books cited by JASIS(T) less than 3 times indicates that JASIS(T) cited monographs diversified. 五、For monographs cited by the JASIS(T), science, social sciences, and bibliography. library science. information resources (general) are the three most cited disciplines. The most commonly cited subjects, which identified from WorldCat and Ulrichsweb.com, are information storage and retrieval systems, human-computer interaction, information retrieval, information science, and cognition. 六、From comprehensive analysis on the discipline of the journals and monographs cited by JASIS(T), it can be found that bibliography. library science. information resources (general), science, and social sciences are the most cited disciplines by JASIS(T). In the other words, these three disciplines are not only the most influential resources, but also are closely related to information science.

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 imitation

Not 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.

metaresearch head score (Codex)0.003
metaresearch head score (Gemma)0.017
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesBibliometrics
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.974
Threshold uncertainty score0.045

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.017
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0260.035
Science and technology studies0.0020.001
Scholarly communication0.0060.004
Open science0.0010.002
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0130.009

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.

Opus teacher head0.147
GPT teacher head0.393
Teacher spread0.246 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

Study designObservational
Domainnot available
GenreEmpirical

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".

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Citations0
Published2009
Admission routes1
Has abstractyes

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