Finding the Holy Grail of Library Value: Characteristics of Academic Libraries at Universities with High Retention Rates
Bibliographic record
Abstract
Objective – What are the characteristics of academic libraries at schools with high retention rates? To help libraries tell the story of their impact, we sought to determine which academic library practices were linked to high retention rates. Methods – The investigators created a survey for the United States Great Lakes region library deans and directors in the Spring of 2022 with 19 questions about their library services and staffing. The survey was sent to 226 schools and had a response rate of 31%. We compared the resulting information to publicly available data on student retention from ACRL Metrics and IPEDS to look for correlations and associations. Results – Statistical analysis used the Chi-squared test and the Pearson correlation to calculate association and correlation. This found six attributes of student connections with library staff and with unique local collections that were associated with statistically significant differences in retention rates and institutions. These attributes were institutions who: used students as archives student workers, used students to staff reference desks, conducted multiple library instruction sessions with the same class, had a staffed archive or special collection space, had an institutional repository that included student work, or had an Instagram account. Conclusion – The survey results gave a clear profile of academic libraries with above average retention, particularly in terms of student focused initiatives and the curation of unique collections. Additionally, the survey gave a foundation, with recommendations, for future researchers to build upon.
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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 | 0.005 | 0.037 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.005 | 0.007 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".