Bibliographic record
Abstract
The digital revolution has transformed reading promotion in a world where technology competes with traditional entertainment, posing challenges for libraries, especially in engaging audiences. Adapting actively is crucial to making reading appealing. The rise of book influencers and reading promoters on social media is pivotal for reading education, offering libraries opportunities to diversify programs. Using the "walkthrough" method on four platforms, our research identified 60 Hungarian book influencers and reading promoters. Structured interviews with 23 influencers aimed to unveil their activities, motivations, and the aspirations and reading experiences behind their content. The study contributes to literary and cultural mediation, exploring the intersection between libraries and literacy promoters. It addresses how literacy promoters can enhance reading promotion in the community, emphasizing their role in generating interest in books and literature. Findings suggest that social media and influencers complement libraries' literacy strategies, benefiting both libraries and readers. Collaborating with influencers can foster a reading culture aligned with the digital era's demands. Encouraging influencers to collaborate could also educate their followers, transforming them into library users.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.008 | 0.005 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.013 | 0.002 |
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".