The Potential of Generic Social Outcomes in Promoting the Positive Impact of the Public Library: Evidence from the National Year of Reading in Yorkshire
Bibliographic record
Abstract
Abstract Objective – To present the development, planning and implementation of a qualitative research project on the impact of National Year of Reading in Yorkshire. Methods – Generic Social Outcomes (GSO) were used to develop a theoretical framework. Data were gathered via in-depth interviews and focus groups with National Year of Reading (NYR) steering group partners in Calderdale and North Lincolnshire, selected as the two case study authorities. The use of MAXQDA computer-assisted qualitative data analysis software (CAQDAS) enabled data and coding structures to be stored and facilitated comparison in this longitudinal study. Results – The findings using the GSO framework show considerable evidence of NYR related activities in supporting the three first-tier social outcomes: ‘Stronger and Safer Communities’, ‘Health and Well-Being’ and ‘Strengthening Public Life.’ Conclusion – The GSO framework can be used by practitioners to help provide evidence for how public libraries contribute to diverse agendas and demonstrate their value to the community. Public library authorities can use this evidence for planning and for advocacy with a range of audiences including local and central government.
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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 | 0.020 | 0.047 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.004 | 0.009 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".