Bibliographic record
Abstract
Libraries must be relevant to the needs of local communities which are becoming more diverse and multicultural. In this paper I will examine the link between inequality, social class and the use of public libraries. I will also build on ground breaking research in the UK - Open to All? The Public Library & Social Exclusion (Muddiman et al, 2000) - and cutting edge good practice in Canada via the Working Together Project (2004-2008). I will outline the practical steps which are required to develop needs-based and community-led library services.My overall theme is Public Libraries & Social Justice (Pateman & Vincent, 2010) and I will explore some of the challenges and barriers to creating socially inclusive libraries and how these can be overcome. I will provide a blue print and a road map for producing strategies, structures, systems and cultures which enable local communities to be fully involved and engaged in the planning, design, delivery and evaluation of their library services.
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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 | 0.005 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.012 | 0.024 |
| Scholarly communication | 0.017 | 0.008 |
| Open science | 0.001 | 0.014 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.011 | 0.001 |
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".