Reading, School Libraries and Equity: A Socio-Spatial Study of School Libraries and Reading in Singapore
Bibliographic record
Abstract
Although Singapore is lauded internationally for its excellent education system, particularly as a top scorer on the international OECD PISA assessments, official educational policies have been criticized for the tendency to sideline socioeconomic status as a factor for school success. To understand the complex space of schooling and how inequity is worked out in practice, I turn to the space of the school library in two contrasting schools to examine how the organization of space can contribute to or inhibit the kinds of learning desired. Using a comparative socio-spatial approach, I map the library spaces of an elite all-boys‟ school and a co-educational government school in Singapore to understand how a space typically associated with the cultivation of reading habits and critical information literacy may in practice serve as a space for differentiated education. Through the physical, social and affective mapping of two school libraries, I describe, breakdown and examine taken-for-granted practices that reveal underlying ideologies governing perception and use of library space. Furthermore, I argue that viewing the school library through socio-spatial lens allows educators a localized, evidence-based framework to evaluate how effective and equitable their school libraries are.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.006 | 0.006 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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; 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".