Bibliographic record
Abstract
This paper explores three Canadian wireless network projects that demonstrate that Wi-Fi technologies, like landline telephones, radio, and hydro, can be used to bring services to local communities. It is our position that despite the strengths and weaknesses of Fredericton’s eZone, Montréal’s Île Sans Fil, and the Lac Seul network in Northern Ontario, these three highlighted Wi-Fi networks demonstrate that a public information utilities model is still a useful lens through which to understand the development and implementation of telecommunications in Canada. Through our case studies, we have observed that in order for municipally based and community Wi-Fi networks to successfully take root in a community, it is advantageous to build on existing technological infrastructure. Moreover, municipal and community needs must be considered in the project. Finally, a cohort of interested advocates from the region is needed. Résumé : Cet article explore trois projets canadiens de réseau sans fil qui démontrent qu’on peut utiliser les technologies Wi-Fi à la manière du téléphone traditionnel, de la radio ou du système hydraulique pour servir les communautés. Selon nous, les réseaux Wi-Fi eZone de Frédéricton, Île sans fil de Montréal et Lac Seul du nord de l’Ontario, quels que soient leurs qualités et défauts, démontrent que le modèle d’un service d’information au public demeure utile pour comprendre le développement et l’établissement des télécommunications au Canada. Au moyen de nos études de cas, nous avons remarqué qu’il est avantageux de se fonder sur l’infrastructure technologique existante pour établir avec succès des réseaux Wi-Fi municipaux et communautaires. Par surcroît, il faut tenir compte des besoins municipaux et communautaires dans un projet. En outre, il est nécessaire d’avoir une cohorte de défenseurs provenant de la région impliquée.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.032 | 0.008 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".