The Governance Challenge of Urban Living Laboratories: Using Liminal ‘In-Between’ Space to Create Livable Cities
Bibliographic record
Abstract
In order to address urban challenges Urban Living Labs (ULL’s) are set up as new forms of partnership between state, (market) and civil society. The primary governance challenge of ULL’s is to effectively use their liminal in-between position to create livable cities. However, liminal space at the same time is claimed to generate certain risks in terms of legitimate decision-making and accountability. By zooming in on the empirical case of ULL’s in a large Dutch city in the Randstad area the authors ask: Which key value trade-offs are made in the liminal space of ULL’s and which new institutional rules emerge in order to deal with these trade-offs? In this chapter the authors identify the following trade-offs: institutional collaboration versus autonomous activism, professional versus lay participation and values, the social versus the material, place bound experimentation versus placeless learning and accountability and capital value versus societal value. Calls for new institutional rules for city making to deal with these trade-offs can potentially address the lack of legitimacy in decision-making, yet may also hamper the open-ended nature of experimentation by introducing bureaucratic procedures and co-opting labs into implementing formal policy.
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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 | 0.000 | 0.001 |
| Science and technology studies | 0.003 | 0.014 |
| Scholarly communication | 0.011 | 0.008 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 0.002 |
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".