Shaping Future Cities: The Impact of Generational Changes on Urban Planning and Key Priorities for Adaptive Strategies
Bibliographic record
Abstract
This study explores the transformative impact of generational shifts on urban planning, shaped by global events, technological acceleration, and evolving social behaviors. By adopting an interdisciplinary approach grounded in sociological and psychological theory, the research investigates how intergenerational dynamics and rapid technological change reshape urban environments. The theoretical framework draws on key thinkers such as Karl Mannheim, Pierre Bourdieu, and Alvin Toffler to contextualize generational identity and conflict in contemporary cities. Through a comprehensive literature review and thematic analysis, the study identifies a transition in urban planning paradigms—from the growth-oriented models of the 20th century to adaptive frameworks that emphasize resilience, digital infrastructure, and economic flexibility. Particular attention is paid to recent disruptive events, such as the pandemic, which have accelerated the need for inclusive, responsive, and multifunctional urban strategies. The findings highlight the necessity of periodic updates to planning instruments—such as master plans and policy frameworks—to accommodate the distinct values, behaviors, and expectations of different generational cohorts. Ultimately, the study advocates for urban policies that promote social cohesion, equitable access to resources, and environmental sustainability. These adaptive strategies aim to foster inclusive cities capable of addressing the complex and shifting demands of both current and future generations.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.010 |
| Scholarly communication | 0.009 | 0.009 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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 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".