MétaCan
Menu
Back to cohort

Citizen Science in Libraries: A Co-Citation Analysis

2025· article· en· W7117243073 on OpenAlexvenueno aff
Ivana Matijević, Dolores Mumelaš, Tomislav Ivanjko

Bibliographic record

VenueCanadian Journal of Information and Library Science · 2025
Typearticle
Languageen
FieldEnvironmental Science
TopicSpecies Distribution and Climate Change
Canadian institutionsnot available
Fundersnot available
KeywordsCitizen scienceField (mathematics)Citizen journalismThematic analysisRelevance (law)Public awareness of scienceOpen scienceScience communicationSociology of scientific knowledgeFunding Agency

Abstract

fetched live from OpenAlex

Citizen science, a core component of the open science movement, emphasizes public participation in scientific research and fosters inclusive, community-driven knowledge production. Libraries are increasingly recognized as critical facilitators of citizen science, offering infrastructure, support, and access to resources. This study investigates the intellectual structure of citizen science within the field of library and information science (LIS) through a co-citation analysis using data retrieved from Web of Science (WoS) and Scopus. The analysis identifies the most frequently co-cited authors and sources, revealing emerging research clusters and thematic trends. Findings show that while citizen science in LIS is a growing area of interest, the field remains relatively fragmented, with limited author interconnectivity and modest citation frequencies. The most frequently co-cited sources include journals focusing on academic and medical librarianship, highlighting the multidimensional relevance of citizen science across subfields. Keyword analysis reveals dominant themes such as open science, crowdsourcing, and digital humanities, which align with libraries’ evolving roles in participatory research. The study provides a comprehensive overview of current research dynamics and collaboration patterns, offering insights into the evolving role of libraries as active participants in citizen science initiatives.

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)0.011
metaresearch head score (Gemma)0.064
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Bibliometrics
Consensus categoriesnone
DomainCandidate signal: Evaluation · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.989
Threshold uncertainty score0.057

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0110.064
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.1150.186
Science and technology studies0.0040.002
Scholarly communication0.0090.005
Open science0.0010.005
Research integrity0.0010.001
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 head0.008
GPT teacher head0.216
Teacher spread0.208 · 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
Published2025
Admission routes1
Has abstractyes

Explore more

Same venueCanadian Journal of Information and Library ScienceSame topicSpecies Distribution and Climate ChangeCategoryBibliometricsFrench-language works237,207