Citizen Science in Libraries: A Co-Citation Analysis
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
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.
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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 arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | Bibliometrics Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Observational | low |
| gpt | Bibliometrics Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Observational | low |
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.008 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.011 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.007 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it