Students as partners in the library
Bibliographic record
Abstract
Students as partners (SaP) is a pedagogical approach where students and instructors learn from one another and contribute to educational decision-making as respected partners (Bovill, 2020;Cook-Sather et al., 2019).SaP is increasingly applied to develop and refine higher education courses and programs (Green, 2019;Scott, 2022;Smith et al., 2021; Struel et al., 2022).However, there is a notable gap in the SaP literature about how libraries, which play an important role in supporting academic achievement and belonging (Oliveira, 2018), partner with students (Salisbury et al., 2020;Germain, 2001).The democratic, participatory mission of libraries aligns with SaP core values, such as respect, mutual decision-making, and shared responsibility.Because libraries support and engage with students in many ways, they are well-positioned for SaP to develop meaningful instruction, programming, and services (Dollinger et al., 2022;Salisbury et al., 2020).Our team of librarian-faculty and students used SaP principles to develop an undergraduate research program within the Purdue Libraries and School of Information Studies.Upon initiating this work, we, Sam and Rachel, two librarian-faculty in the Purdue Libraries and School of Information Studies, recognized the limits of our perspective as educators grounded in libraries.We sought student partners who are accustomed to navigating a variety of disciplinary, professional, and personal identities as they learn to co-develop an inclusive, meaningful, and engaging program focused on information literacy (IL) research.We welcomed Secret, a history graduate student, and Ben, a mechanical engineering undergraduate student, to partner with us to discuss IL, explore student perspectives about undergraduate research, and design outcomes and lessons for what became Student Partners for Information Research and Literacy (SPIRaL), a year-long undergraduate research program where undergraduates conduct original research about IL's role in addressing societal issues that matter to students.This reflective piece explores our student-faculty partnership, highlighting the relational growth that partners experienced within themselves, the team, and beyond the partnership.All four partners reflect on how each partner leveraged personal expertise to strengthen the partnership.We also analyze how the partnership facilitated personal growth.Our experiences and insights coalesced around three themes: (a) identity is individual and communal, (b) trust is
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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 | 0.009 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.015 | 0.007 |
| Scholarly communication | 0.025 | 0.014 |
| Open science | 0.003 | 0.027 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.025 | 0.008 |
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".