URBAN GROWTH: LEGAL, ECONOMIC, AND STRATEGIC IMPLICATIONS. CRACOW AS AN EXAMPLE OF CHALLENGES FACING CENTRAL EUROPEAN CITIES
Bibliographic record
Abstract
Over last decade, Cracow has seen significant expansion, with approximately 100,000 new apartments built. However, much of this development occurred without a clear urban planning strategy, leading to disorder and allowing investors to build freely without clear boundaries. This period of haphazard development coincided with a sharp increase in the city’s population and a rapidly growing economy, driven by foreign direct investments. In response to these challenges, the city has made significant progress in creating a more structured urban environment, with 80% of Cracow now covered by urban planning maps. The city’s key priority was to implement a comprehensive strategy that clearly defines where and what can be built, removing the previous chaos and ensuring better-coordinated development. While the protection and expansion of green spaces is a priority, the focus is on establishing clear guidelines for future growth. The paper also examines efforts to reduce car dependency by improving public transport systems. The paper concludes by emphasizing Cracow’s shift from disorganized growth to a more structured and sustainable approach, with zoning plans ensuring that future development aligns with the city’s long-term urban vision.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.005 | 0.009 |
| Scholarly communication | 0.009 | 0.003 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".