Services for Preschool Children in School Libraries: A Call to Action
Bibliographic record
Abstract
The quality of learning environments and interactions in the first years of life set the stage for school success and have lasting impacts on mental and physical health and wellbeing across the lifespan, and even life expectancy (Wong, Odom, Hume, Cox, Fettig, et al, 2014; Reynolds et al., 2011). Libraries are positioned to provide rich learning opportunities for young children and their caregivers (Institute of Museum and Library Services, 2013, 2015; Urban Libraries Council, 2007). Library programs for young children, offered in nearly all public libraries in the United States support school readiness and educate parents about meaningful ways to interact with their children (Becker, 2012; Burger & Landerholm, 1991; Cahill, Joo, & Campana, 2019; Campana et al., 2016; de Vries, 2008; McKechnie, 2006; Mills et al., 2018; Smardo, 1984; Williams, 1998), but it is unclear what, if any, programs are offered for young children and their families through school libraries.
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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 | 0.028 | 0.043 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.005 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.016 | 0.010 |
| Scholarly communication | 0.017 | 0.022 |
| Open science | 0.010 | 0.023 |
| Research integrity | 0.041 | 0.037 |
| Insufficient payload (model declined to judge) | 0.033 | 0.005 |
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".