The Socioeconomic Profile of Well-Funded Public Libraries: A Regression Analysis
Bibliographic record
Abstract
Abstract Objective – This study aimed to explore the well-established link between public library funding and activity, specifically to what extent socioeconomic factors could explain the correlation. Methods – State-level data from the Massachusetts Board of Library Commissioners were analyzed for 280 public libraries using two linear regression models. These public libraries were matched with socioeconomic data for their communities. Results – Confirming prior research, a library’s municipal funding correlated strongly with its direct circulation. In terms of library outputs, the municipal funding appeared to represent a library’s staffing and number of annual visitations. For socioeconomic factors, the strongest predictor of a library’s municipal appropriation was its “number of educated residents.” Other socioeconomic factors were far less important. Conclusion – Although education correlated strongly with library activity, variation within the data suggests that public libraries are idiosyncratic and that their funding is not dictated exclusively by the community’s socioeconomic profile. Library administrators and advocates can examine what libraries of similar socioeconomic profiles do to receive additional municipal funding.
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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 | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.332 |
| 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; both teacher heads agree on what is shown here.
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".