Improving Information Services to Remote, Rural and Indigenous Populations in Canada
Bibliographic record
Abstract
Twenty-first century libraries are about more than just books “they are about equal opportunity. Free for public consumption, libraries deliver high quality multimedia information resources, professional programming to all ages and stages of life, and shared spaces that promote social wellbeing. Research tells us that quality libraries improve psychosocial development, literacy, citizenship, vocational skilling, recreation, empowerment and local economic growth. Experience tells us libraries are a good financial investment: they return a greater value to the community than they take in funding. Unfortunately, public library service is not as equal as we’d like to think. Public libraries depend heavily on local tax support for their operating budgets, which means small rural, remote, and First Nations communities can’t afford to build the same kind of libraries that make the news in urban centres. They have less programming, fewer resources and computers, and are often open only a few hours each week. In times of economic downturn, when citizens most need to use the local library, or when kids most need the opportunity to explore through reading, remote, rural and First Nations libraries are least likely to be able to help because they are themselves under-resourced. We’re counting on federal, provincial and municipal leaders to prioritize equitable access to information for Canadians regardless of where in this great country our citizens happen to live. This presentation discusses ways to build rural resilience by investing in local libraries and information 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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.028 | 0.004 |
| Scholarly communication | 0.007 | 0.002 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.001 | 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".