Finding Your Place: Assessing Diversity, Equity, and Inclusion in an Academic Library
Bibliographic record
Abstract
Objective – An exploratory study was conducted to identify the key factors that influence students’ perceptions of a sense of belonging in an academic library, focusing particularly on gaining insight into the perspectives of students from historically marginalized communities. Methods – Participants were administered an online survey comprising 18 multiple-choice, Likert-type, and open-ended questions. The survey was active for three weeks during March and April 2022. Effect sizes were calculated using Pearson point-biserial correlation statistics. Qualitative results were coded using thematic analysis. Results – An analysis of the quantitative data revealed that students who identified as non-binary/queer/gender non-conforming, identified as a person of color, or identified as a person with a disability were less likely to find the library as inclusive. They were also more likely to report incidents of microaggression, bias, or discrimination. An analysis of the qualitative data revealed several key factors influencing perceptions of inclusiveness, including space, collections, displays, art, technology, programming, marketing, staff, and wayfinding. Conclusion – These mixed findings suggest that while the majority of students perceive the library environment as inclusive, further efforts are needed to establish a truly inclusive and safe space for students from historically marginalized communities.
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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 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.001 | 0.011 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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".