Fostering a Balanced Library: How Practicing the Principles of Slow Library Movement could lead to a Sustainable Future for Libraries
Bibliographic record
Abstract
The increasing ubiquity of digital information in recent decades has altered the potential form and role of libraries of the future. In order to fulfill their most fundamental mandate, libraries must provide relevant programs and services to their communities. One compelling framework that could guide successful library planning for optimal relevance is that of the Slow Library Movement, first described by Mark Leggott, which involves six key concepts derived from the Slow Food Movement: education, community, local, craftsmanship, people and enjoyment. These concepts have been successfully applied in other disciplines, notably that of education. This paper contains a detailed description, evaluation and elaboration of the framework of the Slow Library Movement.
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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 | 0.017 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.012 | 0.015 |
| Scholarly communication | 0.022 | 0.020 |
| Open science | 0.002 | 0.016 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.009 | 0.003 |
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".