Bibliographic record
Abstract
In advanced, digitalized democratic communities the demands for literacy are a prerequisite for engagement and inclusion, at the same time different forms of divides are omnipresent. By providing access and qualified support to all citizens, public libraries play a central function in the building of democratic and inclusive local communities, being increasingly relied upon by governments to deliver access and support for e-services. Based on a case study of community library services in Sweden, Östergötland, this paper aims to study digital inclusion as reflected in daily practices through the perspective of librarians. In this paper we argue that while advancing digitalisation involves opening of new access and engagement opportunities through empowering digital tools and Internet, it also involves different challenges of exclusion for those who cannot use, choose not to use or have other needs
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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 | 0.013 | 0.039 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.025 | 0.047 |
| Scholarly communication | 0.022 | 0.048 |
| Open science | 0.002 | 0.011 |
| Research integrity | 0.009 | 0.013 |
| 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".