Hackathons for inclusive urban planning: Exploring divergence to co -create convergence
Bibliographic record
Abstract
The VIVA-PLAN project (www.viva-plan.eu) aims to contribute to inclusive urban green planning through developing a new sustainable spatial planning framework for promoting biodiversity, social inclusion and human well-being. The project has a specific focus on marginalized groups such as young people and immigrants. A key element in the VIVA-PLAN case-study approach is the use of two hackathons in each of the case-study areas Urbanplanen (Copenhagen) and Ronna (Södertälje). Traditionally, a hackathon is a multi-day event in which a diverse set of experts and stakeholders is brought together to develop initial solutions to complex problems. Within VIVA-PLAN, the hackathon approach is used to collaboratively find solutions for complex issues related to spatial urban planning at a local scale. The VIVA-PLAN hackathons are embedded within the wider VIVA-PLAN multi-methods approach, featuring a diverse array of social science methods to elucidate local practices related to urban green spaces, including ethnographic investigations, socio-ecological value mapping, interviews and focus-groups and ecological mapping. This document describes the VIVA-PLAN hackathon approach and how the two hackathons are implemented in each of the case-study areas.
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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 | 0.011 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.010 | 0.025 |
| Scholarly communication | 0.016 | 0.017 |
| Open science | 0.003 | 0.034 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.024 | 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".