iPads, Free Data and Young Peoples’ Rights: Refractions from a Universal Access Model During the Pandemic
Bibliographic record
Abstract
The United Nations deemed internet access to be of critical importance for human rights in 2016. In 2020, schools around the world closed during the COVID-19 pandemic. As schools were closed, inequities in internet access gained widespread public attention as many educational opportunities shifted online. Amidst this shift, this paper analyzes an Ontario provincial announcement to provide 21,000 iPads and free data for young people (ages 4-18), during the pandemic. The closure of schools in Ontario, Canada, meant that young people and families who faced technological challenges, such as a lack of devices, stable and affordable internet connections, or sufficient data allowances, could experience barriers to their right to an education. This paper revisits a community informatics (CI) model of internet access, the Access Rainbow, to analyze attempts to operationalize the right to an education through technology in Ontario. In parallel to rights, however, the field of CI faces the ongoing presence of profit-oriented corporations within universal access efforts. This paper argues that socio-technical infrastructural elements of access to the internet became visible through the breakdown of the pandemic. Furthermore, it considers the multi-stakeholder efforts required to implement useful and effective access, where school boards responded in varied ways locally. The paper contributes the concept of refraction to offer continued theorization of a distributive paradigm and a rights-informed approach in community informatics against the backdrop of the pandemic, which could also act as an opening for privatization and disaster capitalism.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.005 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.042 |
| Scholarly communication | 0.009 | 0.010 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 0.000 |
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".