Powering digital communities: How Public Libraries Can Foster Digital Inclusion and Digital Literacy in Ontario
Bibliographic record
Abstract
In the digital age, many aspects of life increasingly rely on the use of technology. In response to this shift toward digitization, it is important to ensure that the people of Ontario not only have adequate access to digital tools, but also the skills to operate such equipment. However, several segments of Ontario’s population, including seniors, low-income individuals, and those living in rural communities, face greater barriers to meaningful participation in the digital economy because of a lack of exposure to digital tools and digital literacy. Numerous institutions and community organizations in the province, including public libraries, are working to bridge the gap in terms of skills and access. As institutions that connect patrons to technologies and digital literacy programs taught by trained literacy professionals, libraries are a crucial resource for fostering a digitally inclusive environment that prepares Ontarians with the skills required to partake in the digital economy.
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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 | 0.004 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.031 | 0.007 |
| Scholarly communication | 0.009 | 0.007 |
| Open science | 0.002 | 0.012 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.017 | 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".