The Right(s) to the Hybrid City and the Role of DIY Networking
Bibliographic record
Abstract
We reconsider the concept of “the right to the city”, introduced by French philosopher Henri Lefebvre, in the light of the new information space that ICTs create in contemporary urban environments. Such spaces include the use of global online social networks, locative media, e-participation platforms, online neighbourhood communities and so forth. Unlike the physical urban space that it overlays, this new and rapidly emerging virtual space has practically no capacity constraints. However, it is subject to inequalities in terms of access, representation, participation, and ownership. In this research note—an interdisciplinary collaboration between a computer scientist and an urban planner—we wish to study the role of wireless technology, which enables the easy deployment of local networks operating outside the public Internet, and the role of the free and open source social software, which facilitates the easy development of customized local applications, allowing citizens to shape their emerging hybrid space. We suggest that this sort of do-it-yourself (DIY) networking can be realised according to citizens’ values, objectives and the particularities of the environment, and could ultimately enable them to compete with large ICT corporations such as Google and Facebook for their “right(s) to the hybrid city”. We employ the urban sidewalk metaphor as an application that is subject to hybrid design and can profit significantly from the special characteristics of DIY networks.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.009 | 0.042 |
| Scholarly communication | 0.011 | 0.012 |
| Open science | 0.001 | 0.009 |
| 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".