Anytime? Anywhere?: Reframing Debates Around Community and Municipal Wireless Networking
Bibliographic record
Abstract
Over the past three years, cities across the United States have announced ambitious plans to build community and municipal wireless networks. The phrase ‘anytime, anywhere’ has had a powerful impact in shaping the way in which debates about these networks have been framed. However, ‘anytime, anywhere’, which alludes to convenience, freedom and ubiquity, is of little use in describing the realities of municipal wireless networks, and, more importantly, it ignores the particular local characteristics of communities and the specific practices of users. This paper examines the media representations and technological affordances of wireless networks as well as incorporating the practices of those that build and use them in an attempt to reframe these debates.
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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 | 0.033 | 0.040 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.005 | 0.009 |
| Science and technology studies | 0.032 | 0.061 |
| Scholarly communication | 0.031 | 0.035 |
| Open science | 0.003 | 0.019 |
| Research integrity | 0.010 | 0.011 |
| Insufficient payload (model declined to judge) | 0.003 | 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".