'Locating Geographic Community in the Information Society: An Atlantic Canadian Perspective on the World Summit on the Information Society'
Bibliographic record
Abstract
This paper is concerned with questions about the role of geographic community in the information society. Specifically, I am interested in the contribution rural communities in Canada can make to the World Summit on the Information Society (WSIS) and their perspective on the practicality of federal connectivity programs enabling their participation in the information society. I argue that the current formations of the information society do not leave much room for community technology in rural areas. In this paper I first outline the WSIS process and provide background on its goals. I then elaborate on rural and remote environments and technology and provide context for the two case sites which provide insights into the situated challenges of the information society in the rural Canadian context. I then argue for the continued importance of geography and the centrality of place within the information society, and discuss the challenges of sustaining community informatics initiatives with these two case illustrations. Finally, I explore the contentious issue of corporate funding of community based technology projects. To frame the discussion of rural Canada, which represents a diversity of communities with different (and often competing) needs, I have chosen to focus on Atlantic Canada, a region in which I have long been interested due to personal experience and its large rural population. I will examine two areas that were chosen as federal “Smart Communities,” a program of the Connecting Canadians agenda administered by Industry Canada. These include the Western Valley of Nova Scotia and the Labrador region.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.049 | 0.030 |
| Scholarly communication | 0.020 | 0.005 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.006 | 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".