Data on librarians' perceptions of participation in a citizen science project in a network of public libraries
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 (CS) is an approach to scientific work and part of the Open Science movement. This study aims to analyse the perception of the librarians about their participation in the aBEIRAr project, which is a CS partnership for the valorisation of the territory developed in the Intermunicipal Network of Libraries of Beiras and Serra da Estrela (RIBBSE) in Portugal. The methodology comprised a literature review, and the case study includes an interview and a survey. Of the results obtained, the following stand out: the libraries are the driving forces behind the aBEIRAr project; they choose the themes, organise and dynamize the activities in their local communities, and establish various partnerships with the mediation of the project's scientific coordination; the level of satisfaction of the librarians in this project is very satisfactory; in the libraries, after carrying out the aBEIRAr project, the number of participants in other face-to-face activities and the interaction on their social network profiles increased; librarians consider that CS can bring to public libraries and their users participative scientific knowledge. The data provides valuable insights into the possibilities and challenges associated with executing CS projects in collaboration with public libraries. These findings contribute to the ongoing discussion about the role of libraries as essential community centers.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.006 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.027 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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