Technological Literacy and Interrupted Internet Access
Bibliographic record
Abstract
As everyday practices are increasingly digitalised, many countries are prioritising broadband rollout. However, infrastructure provision under national policies has not been uniform. In comparison to urban populations, rural communities often have inferior broadband infrastructure and services and there are disparities in digital opportunities between rural and urban areas. A case study of twenty rural Australian internet users reveals that rural communities suffer from limited access, inconsistent and unreliable services, and rural broadband plans’ data restrictions and high prices, which we conceptualise as ‘interrupted access’. Rural internet experiences are subsequently shaped by the availability, speed, stability and affordability of connections. As a response, a form of ‘technological literacy’ is emerging through which rural consumers undertake technical and social manoeuvrings to self-address challenges from interrupted access and increase their opportunities for digital inclusion. Participants developed innovative local solutions, such as self-installed auxiliary hardware and mobile towers, which they used to improve mobile broadband coverage. Those unable to build such solutions displayed new types of knowledge encompassing broader contexts of connectivity (infrastructure, devices and plans) and re-structured rural life to accommodate internet use that is shaped by interrupted access. Growing community-level capacity and interest in broadband development suggests national policies could better reduce rural-urban inequities by supporting and empowering localised solutions. Without addressing the interrupted nature of rural connectivity to improve service quality and affordability, disparities in the digital opportunities available to rural and urban consumers will persist. KEYWORDS: Broadband infrastructure/services, digital divide, rural communities, digital inclusion
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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 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.009 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.000 | 0.007 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".