Framing the Challenges of Digital Inclusion for Young Canadians
Bibliographic record
Abstract
This chapter reports on The eQuality Project’s initial findings from focus groups conducted in Fall 2018 and Winter 2019 with a diversity of youth (ages 13–17) in three Canadian cities about their perspectives and experiences of privacy and equality in networked spaces. Focus groups explored online activities and platforms used by participants, whether and how privacy was an essential aspect to their enjoyment, online experiences where they felt unwelcome or disrespected, and their strategies to mitigate these constraints. We use a modified version of the Institute of Museum and Library Services’ digital inclusion framework to link the perspectives and apprehensions of the young people we interviewed to emerging digital policy questions. These include access (availability, affordability, inclusive design, and public access), application (across various sectors and uses like education, workplaces, employment, economic development, health, public safety, and civic engagement), and adoption (uptake and relevance, privacy and data rights, safety, and digital literacy). We conclude with several policy suggestions, including holding platform companies accountable and transparent about their data collection and privacy protection practises through producing coherent and well-designed terms of service; ensuring funding for enriched digital literacy programming for schools, parents, and young people in order to strengthen digital skills and knowledge about the dynamic nature of datafication; and bringing the voices of diverse Canadian youth into policymaking to ensure that intersectional perspectives and digital justice are core components for a rights-respecting networked environment.
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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 | 0.015 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.009 |
| Science and technology studies | 0.098 | 0.034 |
| Scholarly communication | 0.026 | 0.008 |
| Open science | 0.004 | 0.021 |
| Research integrity | 0.005 | 0.008 |
| Insufficient payload (model declined to judge) | 0.008 | 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".