Public Libraries Can Be Open Science Laboratories for Citizen Science Projects
Bibliographic record
Abstract
A Review of: Cigarini, A., Bonhoure, I., Vicens, J., & Perelló, J. (2021). Public libraries embrace citizen science: Strengths and challenges. Library & Information Science Research, 43(2), 101090. https://doi.org/10.1016/j.lisr.2021.101090Objective – The objective of this study was to evaluate the potential of libraries supporting citizen scientist (CS) projects. Design – Mixed methods program evaluation study. Setting – 24 public libraries in Barcelona, Spain. Subjects – Public librarians and library users. Methods – It is a mixed methods and mixed population study done in several phases. The first phase involved training 30 librarians how to conduct a citizen science project. They were given a pre and post survey about their perceptions of citizen science and comfort-level in conducting a project. The second phase involved a project run by the now-trained librarians with library user participation. At this phase a questionnaire was given to the users at the start and end of the project. Finally, a focus group of librarians was asked about their project. The responses were evaluated through thematic analysis. Seven libraries participated in the focus groups. Main Results – During the first phase of the study, the survey found the librarians were pessimistic about user participation in a citizen science project, both at the beginning (75%) and at the end (79%) of the session. Though they felt confident in discussing citizen science (100%) and had high satisfaction in the training (70%), only 42% felt confident to conduct a project on their own. The second phase involved the users, 94% of whom had never participated in a CS project. At the end, 70% of users said the project positively changed their perceptions of the library and 70% were satisfied with the experiment. During the focus groups, librarians said the project brought new users into the library and had the potential to build more relationships among participants and with the community. Major challenges discussed were user commitment to the project and the workload required by librarians, however they all answered positively when asked about continuing with CS projects. Conclusion – This study showed that citizen science projects can be successfully implemented in public libraries. Public libraries are facing challenges caused by societal change, the rise of open science, and more transparent and novel democratic ways of knowledge production. Updating public library infrastructure would be needed to support these projects more fully. This may involve building partnerships and developing new guidelines. There is potential for public libraries to be leaders and innovators in citizen science.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.013 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.007 |
| Science and technology studies | 0.007 | 0.003 |
| Scholarly communication | 0.017 | 0.020 |
| Open science | 0.003 | 0.030 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.191 | 0.102 |
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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".