Using Academic Librarians and the Academic Library: Survey Results from Mathematics Faculty in United States and Canada
Bibliographic record
Abstract
Despite our diligent outreach and relationship-building efforts, certain groups of faculty within STEM disciplines remain hesitant to engage with academic librarians and utilize library resources. This challenge raises a critical question: How can librarians effectively support a department when faculty members are unresponsive? Our research team, composed of three frustrated mathematics librarians and one mathematics faculty member, recognized the need for a fresh approach. Mathematics, a department known for its independence, has received limited attention in scholarly Library and Information Science (LIS) literature. To address this gap, we embarked on a project to understand mathematics faculty’s perspectives and needs regarding library services. Our research aimed to explore mathematics faculty’s use of library resources and their perceptions of academic librarians. We sought insights into their attitudes, which would inform strategies for more effective collaboration. To achieve this, our team designed a concise 10-minute survey for mathematics faculty across higher education institutions in the United States and Canada. We received 189 responses, providing valuable data for analysis. In this presentation, we will share preliminary findings from our survey conducted in the fall of 2023. The survey included both qualitative (free-text responses) and quantitative (Likert scales and multiple choice) questions. We will also discuss outreach and engagement strategies informed by the survey results, along with examples of faculty perspectives. Additionally, we aim to engage attendees in a discussion to hear their solutions and frustrations in working with less responsive disciplines, such as Mathematics. We believe that many colleagues will have valuable experiences and ideas to contribute, which will help us all collaborate more effectively with some of our more challenging departments. In summary, our project bridges the gap between mathematics faculty and academic librarians by aligning services with faculty needs.
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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 | 0.006 | 0.026 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.021 |
| Science and technology studies | 0.011 | 0.004 |
| Scholarly communication | 0.008 | 0.002 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".