Research Libraries: an Incubator for Science Communication, Public Engagement and Literacy Skills
Bibliographic record
Abstract
Research libraries must play an increasingly important role in society. In an era where questionable and unreliable sources of information abound and quickly proliferate, libraries are more important than ever helping users navigate the tidal wave of information from social media, news sites and other resources and make sense of a complicated and polarized landscape. When science literacy skills, defined as the knowledge and understanding of scientific concepts required for personal decision making and participating in civic and cultural affairs, are combined with information literacy skills, one is empowered to critically interpret and verify science as presented in the media. Science festivals and science engagement events in conjunction with research libraries provide ideal opportunities to combine these two literacy skill sets. A case study will be presented of how the University of Toronto Library, Canada’s largest research library, integrated variegated literacies into science outreach events designed to engage with a diverse community. The public, in its various forms, attends science festivals to engage with scientific experts as well as participate in a range of scientific activities. Programming at science outreach events held within the library will be discussed in how it can serve as a catalyst for deeper learning about information creation, authority and dissemination. For example, students and members of the public took a deep dive into the construction and context of authority by participating in Wikipedia-edit-athons and fake news workshops. By bringing experts out from behind classroom “paywalls” via public lectures and human library events, students and citizens can informally converse and engage in debate with scholars. The backdrop for this case study is the annual Science Literacy Week (SLW) as well as the science communication programming which has stemmed from it. SLW began as a grassroots event in 2014 at the University of Toronto and has since grown and developed into an annual federally funded week-long celebration of science which includes over 800 events put on by over 200 partners in 100 cities across Canada. Many of the 44 libraries at the University of Toronto, including non-science libraries, participate in planning SLW public engagement activities. Since the theme of SLW is changed annually, libraries can capitalise on timely trends and issues such as the concept of post-truth and the notion of information literacy as a social practice. The success of SLW spawned a science engagement portfolio of events held throughout the year within the library. This case study will also discuss the challenges as well as the opportunities of situating a research library as a social space for public engagement. Libraries with their long history of providing democratic access to information provide a natural setting for contemporary public engagement, debate and science communication.
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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 | 0.026 | 0.033 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.027 | 0.024 |
| Scholarly communication | 0.042 | 0.025 |
| Open science | 0.004 | 0.044 |
| Research integrity | 0.008 | 0.009 |
| Insufficient payload (model declined to judge) | 0.025 | 0.011 |
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".