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Record W4313118920 · doi:10.20414/light.v2i1.4914

Identifying and Mapping Study of the Information Professional in Library with Scientometric Analysis

2022· article· en· W4313118920 on OpenAlexaboutno aff
Kamaludin Kamaludin, Abdurrakhman Prasetyadi Abdurrakhman Prasetyadi

Bibliographic record

VenueTHE LIGHT Journal of Librarianship and Information Science · 2022
Typearticle
Languageen
FieldComputer Science
TopicBlockchain Technology in Education and Learning
Canadian institutionsnot available
FundersBadan Riset dan Inovasi Nasional
KeywordsTheme (computing)Digital libraryQuarter (Canadian coin)The InternetLibrary sciencePsychologyComputer scienceWorld Wide WebGeography

Abstract

fetched live from OpenAlex

The development of information in the digital era forces librarians to change their roles to become information professionals who have modern skills to face challenges in the digital environment. This study aimed to determine the extent of the studies conducted on information professionals in libraries and to find out the themes and terms that were often used, the trend of topics each year, and the social networks of the authors. The method used was Scientometric analysis using a single search in the Lens.org database. Articles were searched using the terms “information professional” AND “library” in the title. The data obtained were 1523 publications from 1950 to 2020. The results of this study showed that in 2011 and 2014 the largest number of publications were 76 and 84 articles, respectively. In addition, the average growth rate related to publications among information professionals was quite high at 29% during the analyzed period. The study themes were divided into 4 major theme groups and the basic theme was the most frequently used. Then the term that most often appears was "information" with 1110 repetitions. There were also technical terms such as digital, application, and internet which indicate that the study of information professionals had adapted to systems in the digital era. Following the trend of topics in the third quarter (2013-2020), it showed more about the LIS and skills.

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
Observationallow
gptBibliometrics
Domain: not available · Genre: Empirical
About the Canadian research system: no · About a Canadian topic: no
Observationallow
models agreeAgreement compares identical category sets and study designs across arms.

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)0.013
metaresearch head score (Gemma)0.054
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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.914
Threshold uncertainty score0.068

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0130.054
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0860.092
Science and technology studies0.0020.001
Scholarly communication0.0070.005
Open science0.0010.003
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0040.001

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 head0.016
GPT teacher head0.246
Teacher spread0.230 · 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.

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".

Quick stats

Citations0
Published2022
Admission routes1
Has abstractyes

Explore more

Same venueTHE LIGHT Journal of Librarianship and Information ScienceSame topicBlockchain Technology in Education and LearningCategoryBibliometricsFrench-language works237,207