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Record W7005565218

Research Productivity and Publication Trends of ‘Law Library Journal’ (1989-2021): A Scientometric Study

2024· article· en· W7005565218 on OpenAlexaboutno aff

Bibliographic record

VenueLincoln (University of Nebraska) · 2024
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicBlood disorders and treatments
Canadian institutionsnot available
Fundersnot available
KeywordsScientometricsProductivityLaw libraryBibliometricsPeriod (music)Scientific literatureDistribution (mathematics)Trend analysis
DOInot available

Abstract

fetched live from OpenAlex

Structured Abstract: Purpose: The researcher has conducted a quantitative analysis of literature published in the Law Library Journal (LLJ) during the period of 1989 to 2021 (thirty-two years) using a series of scientometrics indicators. Such as annual scientific production, year-wise distribution of articles, and their average citations. i.e. most locally cited authors and most frequent word occurrences, the frequency distribution of scientific productivity by Lotka’s law, top-five most cited documents, most relevant affiliations, and top twenty prolific authors engaged in Law Library Journal. Addition to that the co-authorship pattern author-wise, organization-wise and country-wise, and country-wise scientific production, institution’s collaboration network and countries collaborations, WordCloud’s most frequent author keywords. Design and Methodology: The researcher has used the Web of Science database for retrieving the desired sample. In total 2171 publications records were considered for the literature analysis considering their relevancy. Biblioshiny and VOS viewer software is used to create tarious maps and visualization. Findings: The study found that Law Library Journal has received most citations (2565), followed by Legal Reference Service (339) citations Journal of Legal Education with (330) citations. Law Library Journal's impact has been significant with an h-index of 18, with a g-index of 23, having total citations of 2980 with 732 for the period from 1989-2021. Study reveals that the highest number of research articles were published by Whisner M. with 78 articles, out of which 77.03 Articles were Fractionalized and he has received 260 citations, followed by Houdek FG with 27 articles out of which 22.84 Articles were Fractionalized and he received 126 citations. Lotka’s law reveals about 0.677% of the authors (1013 authors) have one publication, and 0.178 % of the authors (267 authors) have two publications. University of Washington (106) was the dominant institute in-terms of highest number of papers published, Fallowed by University of California (62), followed by University of Corolina (44). Danner RA has the highest h-index (5) and g-index (8) with 11 number of articles and 638 citations, followed by Mark SN who has the second rank, h-index (5) and g-index (5) with 5 articles and 42 citations, and Whisner M who has the h-index (5) and g-index (6) with 43 articles and 110 citations. USA leads the publications with 1747 articles; however, many are in co-authorship with at an author of at least one other country. Subsequently Canadian authors have produced 24 articles, followed by United Kingdom. The term ‘Law’ has occurred 37 times and the term ‘Information’ has appeared 16 times, followed by History (12), Legal Research (11), Access (10), Future (9), Library (9). Berring RC, 1989, Law Libr J. has received most citations with first rank (LC-20) and (GC-23). Research Limitations: The study exclusively deals with the documents published in the Law Library Journal during 1989 to 2021 and indexed in Web of Science database. Thus, documents which are not covered in web of science are excluded from the purview of research. This study is significant in order to measure the impact of Law Library Journal and useful to identify the research patterns from publications and of developments in the field of Law librarianship.

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

Direct model labels (unvalidated)

Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.

Model armCategoriesStudy designConfidence
gemmaBibliometrics
Domain: not available · Genre: Empirical
About the Canadian research system: no · About a Canadian topic: no
Not applicablelow
gptBibliometrics
Domain: not available · Genre: Empirical
About the Canadian research system: no · About a Canadian topic: no
Other designhigh
models splitAgreement compares identical category sets and study designs across arms.

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.745
Threshold uncertainty score0.289

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.028
GPT teacher head0.300
Teacher spread0.272 · 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

Labeled directly by 2 models reading the full record.

Bibliometrics

The models disagree on parts of this classification; every voice is preserved in the section at the end of the page.

Study designNot applicable · Other design
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".

Quick stats

Citations0
Published2024
Admission routes1
Has abstractyes

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