Data on librarians' perceptions of participation in a citizen science project in a network of public libraries
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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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 | 0.010 | 0.037 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.005 | 0.007 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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.
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".