The public utility and the public park: Metaphors and models for community-based Wi-Fi networking
Bibliographic record
Abstract
The rising and falling fortunes of municipal wireless networking projects in the United States have raised questions about whether and how Wi-Fi connectivity should be provided as a public service. Two metaphors provide ways of thinking about the purpose of public Wi-Fi. This paper discusses how the ldquopublic servicerdquo and ldquopublic parkrdquo metaphors for Wi-Fi networking can be applied to Frederictonpsilas Fred-eZone project, North Americapsilas first municipally owned free Wi-Fi network, as a means of comparing it with other North American Wi-Fi networks, especially Montrealpsilas Ile Sans Fil network, which also uses hotspots. Specifically, the paper describes how different metaphors can assist network planners in determining the scope of their project. The ldquopublic utilityrdquo metaphor for Wi-Fi networks focuses on the potential for Wi-Fi to act as a type of internet access infrastructure. In contrast, the ldquopublic parkrdquo metaphor concentrates on the symbolic space of sociability, play, and democratic engagement that Wi-Fi networks could create. This metaphor suggests that Wi-Fi could be used as a form of media. Municipalities considering Wi-Fi networks can learn from applications of these metaphors.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.008 | 0.030 |
| Scholarly communication | 0.008 | 0.014 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".