Bibliographic record
Abstract
Purpose Librarians planning for the future and unsure about the place of books in an age dominated by technology and media need evidence to make sound decisions. Library and information science researchers have studied the impact of pleasure reading on individuals but not on society. The purpose of this paper is to raise awareness about the benefits of recreational reading for societies and to consider the implications of these findings for libraries. Design/methodology/approach Examining a wide range of studies by government bodies, intergovernmental agencies and academics, this paper addresses a gap in the library literature by critically evaluating the combined implications of sources not hitherto viewed together. Findings The more leisure books people read, the more literate they become, and the more prosperous and equitable the society they inhabit. Practical implications Librarians should create a more robust culture of reading and play a stronger advocacy role for books in libraries. Originality/value No one has yet examined government reports about literacy in relation to studies on the impact of pleasure reading. The implications of this combined research highlight the fact that pleasure reading benefits societies as well as individuals, a finding that has significant implications for the future direction of libraries. Decision-makers who need a robust mandate for book-focused resources and services will find supportive statistical evidence in this paper.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.006 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".