<b>USE</b><b> </b><b>OF</b><b> </b><b>SCHOOL</b><b> </b><b>LIBRARY</b><b> </b><b>IN</b><b> </b><b>DEVELOPMENT</b><b> </b><b>OF</b><b> </b><b>READING CULTURE HABITS</b>
Bibliographic record
Abstract
Background: The global decline in adolescent reading culture, intensified by digital media proliferation, presents a significant pedagogical challenge. School libraries are posited as vital antidotes, yet their efficacy hinges on access, quality, and guided mediation. This is particularly critical in exam-oriented educational systems like Pakistan's. Objectives: This study aimed to (1) explore how secondary students utilize school library reading materials, (2) determine the quantitative relationship between library use and reading culture development, and (3) investigate the mediating role of English Language Teachers (ELTs) in this process. Methods: A convergent mixed-methods design was employed in a private secondary school in Karachi. Quantitative data from 80 students were collected via a closed-ended survey and analyzed using descriptive statistics, Pearson’s correlation, and linear regression in SPSS (v23). Qualitative data from 25 ELTs were gathered through open-ended questionnaires and analyzed using thematic analysis. Results: Quantitative analysis revealed a significant positive correlation between library use and reading culture (*r* = .421, *p* < .000). Regression confirmed library use as a significant predictor (β = .421, *p* < .000), explaining 17.7% of the variance in reading culture. Students primarily used the library for academic tasks (assignments: 85%, research: 78%). Thematic analysis of teacher responses yielded seven core themes: (1) Teachers as Motivators and Guides, (2) Facilitating Physical Access, (3) Student Perception as an Academic Resource, (4) The Digital Disruption, (5) Systemic Constraints, (6) Collaborative Advocacy with Administration, and (7) Pedagogical Integration of Library Resources. Conclusion: While school library use significantly impacts reading culture, its potential is largely confined to academic reinforcement due to systemic constraints: insufficient library periods, outdated collections, and an exam-centric syllabus. ELTs are active, willing mediators but require structured institutional support. Cultivating a genuine, pleasure-based reading culture necessitates a tripartite strategy: administrative investment in relevant, diverse library stocks and schedule allocation; teacher-led pedagogical innovation; and a conscious effort to balance academic and leisure reading within the school ecosystem.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.092 | 0.031 |
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".