Fostering Reading Motivation through a Community-Based Book Fair: A Case Study in a Romanian Middle School
Bibliographic record
Abstract
This research article presents a case study exploring the motivational impact of a community-driven book fair held in a Romanian public middle school. The initiative, organized at Scoala Gimnaziala “Mihai Viteazul” in Târgoviște, aimed to foster reading engagement by promoting voluntary book donations, student-led organizing teams, and increased literary access. The event spanned three weeks and involved over 1000 students at various levels of participation. A pre- and post-intervention five-item questionnaire was administered to a selected group of 78 students aged 11–14, complemented by systematic classroom observations conducted by educators. Findings indicate significant improvements in students’ intrinsic motivation to read, perceived access to books, and peer-based reading dialogue. These results highlight the potential of school libraries to act as dynamic agents of literacy development through informal and community-based approaches. The study also outlines challenges related to sample size, generalizability, and the reliance on self-reported data, offering suggestions for future longitudinal research. Ultimately, this article contributes to the growing body of literature on school-based reading interventions and supports the evolving role of librarians as facilitators of student agency and participatory literacy culture..
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.012 | 0.005 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".