Building a Successful Reading Culture through the School Library: A Case Study of a Singapore Secondary School
Bibliographic record
Abstract
Much research has documented the strong correlation between independent reading and academic achievement, and the school library can serve a crucial role in encouraging reading. Drawing from one case study out of a larger dataset of six schools, this paper details how one school transformed its school library and made it a central place for reading within the school. Data collected provided evidence of the kinds of strategies, programmes and design that works to encourage reading. Schoolwide reading surveys, interviews with principals, teachers and Library coordinators at each school and interviews with students gave an understanding of the culture of reading and library use within the school. Qualitative library observations, timed counts, narratives and time-lapse photographs of library space contribute to our understanding of how particular spaces within the library was used for reading or not. Factors for building a reading culture include: (1) Designing conducive spaces for reading, (2) Curating the selection for readers, (3) Creating programmes to excite readers, (4) Designing spaces for reading, and (5) Building an ecology for reading.
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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 | 0.005 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.017 | 0.007 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".