Towards Conviviality in Smart Territories
Bibliographic record
Abstract
Today, smart planning models are beginning to show aspects of fragility, with Sidewalk Labs’ abandonment of the Quayside Toronto project being an emblematic example. This fragility indicates the need to deconstruct these powerful narratives and reflect on the appropriate application of ICT devices to spatial planning. The article aims to analyse the power of the dominant narrative, that of the smart city, which confers to the models of territorial development a conceptual value finalised in the production of economic value. The smart models present a series of limitations: Spatial ones, by creating a disconnection between physical and digital spaces; Political ones, by reducing territorial governance to its technical dimension; Social ones, by externalising and privatising public services. These limitations imply the delegation of the control of these technological devices with which we interact. After the analysis of smart planning, the article will explore an alternative approach regarding the use of technology. This approach will be based on Ivan Illich’s conviviality theory, and will aim to examine the development of smart territories as a place of reintegration of social relations and community in order to outline a different relationship with technology. Could technology establish an ethical relationship between territory and community?
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.042 |
| Scholarly communication | 0.009 | 0.007 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 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".