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Record W4415624213 · doi:10.29173/slw8817

Fostering Reading Motivation through a Community-Based Book Fair: A Case Study in a Romanian Middle School

2025· article· W4415624213 on OpenAlexvenueno aff
Raluca Ispas

Bibliographic record

VenueSchool Libraries Worldwide · 2025
Typearticle
Language
FieldPsychology
TopicReading and Literacy Development
Canadian institutionsnot available
Fundersnot available
KeywordsRomanianReading (process)Agency (philosophy)Reading motivationLiteracyPsychological interventionSample (material)

Abstract

fetched live from OpenAlex

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Science and technology studies, Scholarly communication, Research integrity, Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.532
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0020.004
Science and technology studies0.0020.000
Scholarly communication0.0020.003
Open science0.0010.002
Research integrity0.0000.004
Insufficient payload (model declined to judge)0.0020.000

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.

Opus teacher head0.071
GPT teacher head0.327
Teacher spread0.256 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2025
Admission routes1
Has abstractyes

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