Demographic Variables Are Associated with Differing Perceptions of a Broad Range of Public Library Benefits
Bibliographic record
Abstract
Objective – To determine the frequency and nature of perceived beneficial outcomes of public libraries on individuals, and to identify demographic differences in these perceived outcomes.
 
 Design – Self-administered, online questionnaire asking respondents to rate the frequency of benefits they received from public libraries in 22 areas of life including education, work, and business; everyday activities; and leisure activities.
 
 Setting – United States of America.
 
 Subjects – 1010 respondents from 49 states: 50% female, 76% white, 55% urban or suburban. 
 Methods – Correspondence analysis was used to visualize relationships between demographic variables and perceived outcomes. Exploratory factor analysis was used to identify structures among the outcomes and summarize data into three core dimensions: everyday activities and interests; reading and self-education; and work and formal education. Multiway ANOVAs were used to test the significance of demographic differences on perceived outcomes.
 
 Main Results – The most highly ranked areas of perceived benefits were reading fiction and non-fiction, self-education during leisure time, interest in history or society, and health. Outdoor activities, exercise, and sport ranked the lowest. Respondents in younger age groups reported benefits in “education and work,” as did ethnic minorities and people with lower household incomes. “Everyday life” benefits were reported by male, suburban, White, middle-income respondents. “Reading and self-education” benefits were reported by high-income, older age groups, White, and female respondents. Two demographic groups did not correspond to any benefit categories: those who did not graduate high school and those over age 65.
 
 Conclusion – There are significant differences among demographic groups in how the benefits of public libraries are perceived, and these demographic differences have implications for program planning, marketing, and outreach in public libraries. Specifically, libraries should work to increase and improve service to less-advantaged groups, including low-income earners and ethnic minorities, and make available more services and resources relevant to older people.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.323 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 teacher head, 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".