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Record W2099424129 · doi:10.1002/asi.22645

A bibliometric chronicling of library and information science's first hundred years

2012· article· en· W2099424129 on OpenAlexaff
Vincent Larivière, Cassidy R. Sugimoto, Blaise Cronin

Bibliographic record

VenueJournal of the American Society for Information Science and Technology · 2012
Typearticle
Languageen
FieldComputer Science
TopicWeb visibility and informetrics
Canadian institutionsUniversité de MontréalUniversité du Québec à Montréal
Fundersnot available
KeywordsInformation scienceDominance (genetics)Library scienceField (mathematics)CitationCitation analysisSubject (documents)BibliometricsInterdisciplinaritySociologySocial scienceComputer scienceMathematics

Abstract

fetched live from OpenAlex

This paper presents a condensed history of Library and Information Science ( LIS ) over the course of more than a century using a variety of bibliometric measures. It examines in detail the variable rate of knowledge production in the field, shifts in subject coverage, the dominance of particular publication genres at different times, prevailing modes of production, interactions with other disciplines, and, more generally, observes how the field has evolved. It shows that, despite a striking growth in the number of journals, papers, and contributing authors, a decrease was observed in the field's market‐share of all social science and humanities research. Collaborative authorship is now the norm, a pattern seen across the social sciences. The idea of boundary crossing was also examined: in 2010, nearly 60% of authors who published in LIS also published in another discipline. This high degree of permeability in LIS was also demonstrated through reference and citation practices: LIS scholars now cite and receive citations from other fields more than from LIS itself. Two major structural shifts are revealed in the data: in 1960, LIS changed from a professional field focused on librarianship to an academic field focused on information and use; and in 1990, LIS began to receive a growing number of citations from outside the field, notably from Computer Science and Management, and saw a dramatic increase in the number of authors contributing to the literature of the field.

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)0.017
metaresearch head score (Gemma)0.062
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesBibliometrics
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.908
Threshold uncertainty score0.088

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0170.062
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0920.140
Science and technology studies0.0050.006
Scholarly communication0.0150.015
Open science0.0010.006
Research integrity0.0020.003
Insufficient payload (model declined to judge)0.0030.001

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 head0.013
GPT teacher head0.262
Teacher spread0.249 · 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 designNot applicable
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

Citations176
Published2012
Admission routes1
Has abstractyes

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