Various Factors May Influence High School Student Use of Public Libraries
Bibliographic record
Abstract
Objective – To discover the factors that influence frequency of high school students’ usage of public libraries. Design – Structural equation modeling (SEM) using the person-in-environment (PIE) framework to test latent variables and direct and indirect relationships between variables. Setting – Public and school libraries in the United States. Subjects – Three datasets: Educational Longitudinal Study of 2002, the National Center for Education Statistics (NCES), provides data about individual students; Public Libraries Survey of 2004, then conducted by NCES, provides data about public libraries in the United States; and Summary Files 1 and 3 of U.S. Census 2000, provide neighborhood-level demographic data. Methods – Using ArcGIS, the researcher prepared and linked three datasets. Data were analyzed using factor analysis, regression, weighted least squares, and path analysis in order to test relationships between variables exposed in three large datasets. Main Results – Frequency of public library use by high school students may be influenced by several factors, including race and/or ethnicity and access to resources like school libraries, home computers, and public libraries with adequate service levels. Conclusion – Increased funding for public library spaces and resources may be warranted by the finding that high levels of public library service may increase high school students’ use of public libraries, particularly in socioeconomically disadvantaged neighborhoods.
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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 | 0.001 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.012 | 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".