"Last mile" or local innovation?: Canadian perspectives on community wireless networking as civic participation
Bibliographic record
Abstract
In the rush to solicit corporate bids for WiFi coverage, North American municipalities may be overlooking the capacity of existing community wireless networking (CWN) projects to not only provide WiFi service, but to mobilize civic participation. CWNs, where citizens experiment with, install, and maintain information infrastructures based on 802.11x wireless equipment, may be providing new opportunities for participation in civic life, including developing innovative technical and economic solutions to local problems, developing WiFi as a platform for community media, and mobilizing new spaces for political advocacy. The data presented in this paper assesses four Canadian urban CWNs: Montreal’s Ile Sans Fil, WirelessToronto, Vancouver-based British Columbia Wireless Networking Society and the newly-formed Ottawa Gatineau Wi-Fi. The adaptation of each of these groups to their local policy and technical environments reveals the importance of local, grassroots players in the WiFi universe, as both innovators and policy-makers. Canadian CWNs provide good examples of how grassroots innovation can emerge in competition and cooperation with other types of service provision. Furthermore, the activities of these CWNs underline the importance of safeguarding the network and policy spaces that allow communities to flourish.
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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 | 0.004 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.007 |
| Science and technology studies | 0.034 | 0.019 |
| Scholarly communication | 0.014 | 0.007 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.005 | 0.006 |
| Insufficient payload (model declined to judge) | 0.013 | 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".