Leaving the Library: How We Improved Information Literacy by Joining Our User Communities
Bibliographic record
Abstract
Presentation at the Workshop for Instruction in Library Use (WILU) held in Winnipeg, MB, Canada, May 22-24, 2019. How can we develop a better understanding of the goals of our user communities and what they’re trying to accomplish? What can we do to ensure that our information literacy goals and initiatives align with what our students need to learn? How do we demonstrate our value and expertise to our user communities? One strategy is to disrupt where we practice librarianship. By practicing librarianship solely in the library, our practice is shaped mainly by the library. Moving out of the library and inhabiting the space where our students and faculty work gives librarians opportunities to engage with and develop strong working relationships with our program faculty and stakeholders. We can then use these strong working relationships to better learn about the culture, goals, and needs of our user communities and align our information literacy goals and initiatives with them. By focusing our information literacy initiatives to what will have the biggest impacts on our user communities, and through partnerships with faculty and campus stakeholders, we become seen as a valuable partner in problem-solving and meeting their goals. Our practice of librarianship becomes informed by and integrated into our user communities. This presentation describes the process of getting librarians out of the library and engaged with their user communities at the University of Michigan-Dearborn and the University of Michigan, Ross School of Business. We also discuss strategies that librarians used to build relationships with faculty and other stakeholders in their program areas as well as those used to learn about the program’s culture, goals, and needs. Librarians were able to leverage this into integrated information literacy initiatives tailored to these goals and needs and developed in collaboration with partners in their user communities, which had a greater impact on desired student outcomes. This increased the perceived importance of information literacy learning and awareness of librarian expertise among program faculty and stakeholders, who also found it easier to collaborate with their librarians. It also became easier and more motivating for students to consult their librarian and use library resources. By moving into the spaces where our students and faculty work and learn, we were able to develop high-impact information literacy goals and initiatives aligned with those of our user communities and demonstrate our value.
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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 | 0.030 | 0.046 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.037 | 0.011 |
| Scholarly communication | 0.024 | 0.029 |
| Open science | 0.004 | 0.024 |
| Research integrity | 0.006 | 0.008 |
| Insufficient payload (model declined to judge) | 0.017 | 0.005 |
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".