Imagining the City of Tomorrow Through Foresight and Innovative Design
Bibliographic record
Abstract
Ecological and digital transitions alongside concerns over social inequalities have signalled the advent of complex new challenges for contemporary cities. These challenges raise issues pertaining to the dynamic capability of urban planners: more specifically, their ability to revise their tools and planning routines in urban projects. New paradigms of collective action for the transition towards innovative cities have been developed in large organisations. European companies, especially in public transportation, have developed such tools based on innovative design theories. One of these methodological tools, the Definition-Knowledge-Concept-Proposition (DKCP) process, was used to generate a new range of planning options for an urban district in Montreal, Canada. For many municipal organisations, the formulation of innovative ideas only concerns one stage of the process, represented by the ‘P’ phase. However, innovative routines should rather include the earlier phases of identifying the scope of possible innovations, the search for intriguing knowledge and disruptive design activities. The desire to tackle the complex challenges of 21st century cities has led to a new professional identity: the ‘innovative urban planner’.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.011 | 0.048 |
| Scholarly communication | 0.014 | 0.015 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.008 | 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".