Crisis and reorganization in urban dynamics: the Barcelona, Spain, case study
Bibliographic record
Abstract
We use adaptive cycle theory to improve the understanding of cycles of urban change in the city of Barcelona, Spain, from 1953 to 2016. More specifically, we explore the vulnerabilities and windows of opportunity these cycles of change introduced in the release () and reorganization () phases. In the two recurring cycles of urban change analyzed (before and after 1979), we observe two complementary loops. During the front loop, financial and natural resources are efficiently exploited by homogenous dominant groups (private developers, the bourgeoisie, politicians, technocrats) with the objective of promoting capital accumulation based on private (or private-public partnership) investments. During the back loop, change is catalyzed by heterogeneous urban social networks (neighborhood associations, activists, squatters, cooperatives, nongovernmental organizations) whose objectives are diverse but converge in their discontent with the status quo and their desire for a "common good" that includes social justice, social cohesion, participatory governance, and well-being for all. The heterogeneity of these social networks (shadow groups) fosters learning, experimentation, and social innovation and gives them the flexibility that the front loop's dominant groups lack to trigger growing pressures for transformation, not only within, but also across spatial and temporal dimensions, promoting panarchy. At the end, the reorganization phase () becomes a competition or negotiation between potential directions and outcomes (including conservative leanings and intentional bottom-up change) to restore the former system.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".