FUTURE (From Urban to hUman Regeneration): Systemic co-design in four European cities
Bibliographic record
Abstract
Contemporary urban regeneration practices remain limited in their capacity to effectively incorporate multiple stakeholder inputs and provide equitable solutions to urban problems. The European Union Erasmus+ funded FUTURE (From Urban to hUman REgeneration) project provides knowledge and capacity to students and professionals in government, business, and community to underscore human-centred system design in our communities. Target groups come from diverse disciplines across the regeneration sector, united in their need for better ways to engage citizens in their work and process. The FUTURE programme responded to a lack of community-based engagement mechanisms, cognisant of the need for human-centric system design approaches across the built environment sector. The project piloted practice-based training, creating a curriculum to equip participants with knowledge in the co-production of frameworks and approaches as well as the skills to deliver solutions with and for communities. In the Dublin pilot, students learned how to frame societal challenges from the perspective of the community in Ballymun, once the location for Europe’s largest urban renewal project. Following a qualitative research approach, students practised community engagement techniques to centre the lives of residents in the process. Making clear the significance of evidence-based planning, learners identified specific spatial challenges that might be addressed in Integrated Action Plans (IAPs) toward shared outcomes for the community. FUTURE has succeeded in developing a curriculum capable of adapting to local conditions and being suitable for a range of participants, including master’s level students, civil servants, design practitioners, and community and business leaders. The living lab component generated valuable experience in delivering this curriculum in a real-world setting. The work of FUTURE is available as a playbook for scaling this impactful systemic co-design programme.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".