Using Analytic Tools with California School Library Survey Data
Bibliographic record
Abstract
Abstract
 
 Objective — California school libraries have new state standards, which can serve to guide their programs. Based on pre-standard and post-standard library survey data, this research compares California school library programs to determine the variables that can potentially help a school library reach the state standards, and to develop a predictive model of those variables.
 
 Methods – Variations of decision trees and logistic regression statistical techniques were applied to the library survey data in order to create the best-fit model. 
 
 Results – Best models were chosen within each technique, and then compared, concluding that the decision tree using the CART algorithm had the most accurate results. Numerous variables came up as important across different models, including: funding sources, collection size, and access to online subscriptions.
 
 Conclusion – School library metrics can help both librarians and the educational community analyze school library programs closely and determine effective ways to maximize the school library’s impact on student learning. More generally, library resources and services can be measured as data points, and then modeling statistics can be applied in order to optimize library operations.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.004 | 0.899 |
| Open science | 0.001 | 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".