Advocacy and evidence for sustainable public computer access
Bibliographic record
Abstract
Purpose This paper aims to draw together the evidence‐based advocacy experience of five national programs focused on developing public access information and communications technologies (ICT) via public libraries as grantees of the Bill & Melinda Gates Foundation's Global Libraries Initiative. Design/methodology/approach The authors describe a common approach to strategic advocacy and to impact planning and assessment. They then outline the experience of each program in using a range of evidence to help meet specific advocacy objectives. They give particular attention to how each program is using specific evidence to convince key players of the importance of public access ICT provided by public libraries in meeting the objectives of the key players. Findings This collective experience shows that when advocating at the national level, statistical data and empirical evidence can demonstrate that public libraries contribute to stakeholders' goals. Such data can include technology skills that users have gained as well as how users improve their businesses, become better educated, and access government services. Common denominators from the programs include a disproportionate positive impact achieved (or anticipated) in rural communities and on relatively disadvantaged groups such as older workers, old people and unemployed people. Practical implications Both the general approach to evidence‐based advocacy described and the specific messages about targeting advocacy efforts on key players and on the service users who are most likely to benefit from public access ICT are of potential value to anyone planning a national, regional or local advocacy program focused on public libraries and their services. Social implications As the paper deals with global library advocacy issues, and impact planning, it is hoped it is a step towards more measurable social impact for libraries. Originality/value This is the first full public report of the Global Libraries approach to evidence‐based advocacy as conducted in the five countries represented in the paper. It is part of a steadily growing body of knowledge being amassed by Global Libraries about effective provision of public access ICT via public libraries in a range of countries.
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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 | 0.114 | 0.327 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.008 | 0.006 |
| Science and technology studies | 0.004 | 0.028 |
| Scholarly communication | 0.021 | 0.019 |
| Open science | 0.004 | 0.018 |
| Research integrity | 0.008 | 0.009 |
| Insufficient payload (model declined to judge) | 0.013 | 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".