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Record W2724862765 · doi:10.24193/jssp.2017.1.06

How Green are Romania’s Cities? A Quarter - Century of Green Area Policy

2017· article· en· W2724862765 on OpenAlexaboutno aff
Bogdan-Nicolae Păcurar

Bibliographic record

VenueJournal of Settlements and Spatial Planning · 2017
Typearticle
Languageen
FieldEnvironmental Science
TopicUrban Green Space and Health
Canadian institutionsnot available
FundersUniversitatea Babeș-Bolyai
KeywordsQuarter (Canadian coin)GeographyRegional scienceArchaeology

Abstract

fetched live from OpenAlex

Cities, on a highly basic level, comprise of functional areas, such as: residential areas, industrial areas, transport areas, service areas, green areas etc.Even though they are often mentioned at the end of most enumerations, case in point, green areas and parks in particular are crucial for the proper growth and development of any city on the planet.However, the intention of this paper is not to present the merits of such spaces but rather to focus on how these urban open spaces have evolved in time.Our analysis will be limited to the period between 1993 and 2015 and will use Romania as case study.More specifically, the paper will examine how urban planning and legislative policies have influenced the evolution of green areas, essentially what happened after the collapse of the planned economy, the socialist regime and the top-down planning practice and with the emergence of a more aggressive urban development and a market real estate economy.The analysis will include Bucharest (Romania's capital city) and the 41 county seats. THEORY AND METHODOLOGYWith urban population exceeding 50% of the world population and expected to increase to 66% by 2050, the quantity and quality of green spaces in urban areas have become key elements for sustaining the quality of human life in urban areas.Thus, the study and research of green areas and their role in the well-being of cities and its citizens has become not only of chief interest for scientists and urban theoreticians, but also urgent for local authorities and communities.Reviewing the more recent scientific literature on the matter, one can observe two main research streams when it comes to the study of green areas: 1. the influence of such spaces on different aspects of urban life and 2. the territorial dynamics of green areas.The former path includes, for instance, the study conducted by Morancho ( 2003) who found a Centre for Research on Settlements and Urbanism Journal of Settlements and Spatial Planning J o u r n a l h o m e p a g e: http://jssp.reviste.ubbcluj.roGreen areas are one of the most essential -but more often than not overlooked by planners, officials, and local governmentsfunctional parts of a city.This paper examines the evolution of such zones in 42 of the most important urban areas of Romania (the county capital cities) focusing on two years, 1993 and 2015.Furthermore, the paper links the evolution of green area developments to different legislative policies and planning practices that emerged after the fall of communism and the emergence of capitalism in this part of Europe.We conclude that the apparent expansion of green areas masks an unfortunate and somewhat dangerous situation, as a quarter of a century of territorial, regional, and local planning has failed to create not only good public green areas, but also enough square meters compared to built areas.Solutions can be found in improving the existing legislation and implementing new, more steadfast urban development schemes that put green areas at their very core.

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 imitation

Not 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.

metaresearch head score (Codex)0.004
metaresearch head score (Gemma)0.004
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.047
Threshold uncertainty score0.105

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.002
Science and technology studies0.0050.011
Scholarly communication0.0060.004
Open science0.0010.004
Research integrity0.0030.005
Insufficient payload (model declined to judge)0.0050.000

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.

Opus teacher head0.026
GPT teacher head0.273
Teacher spread0.247 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations6
Published2017
Admission routes1
Has abstractno

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