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A systematic review of motives for densification in Swedish planning practice

2020· review· en· W3106822974 on OpenAlexaboutno aff
Per Haupt, M.Y. Berghauser Pont, Victoria Alstäde, Per Berg

Bibliographic record

VenueIOP Conference Series Earth and Environmental Science · 2020
Typereview
Languageen
FieldEnvironmental Science
TopicUrban Green Space and Health
Canadian institutionsnot available
Fundersnot available
KeywordsQuarter (Canadian coin)Scientific literatureEmpirical evidenceSustainable developmentScientific evidenceUrban planningEnvironmental planningPolitical scienceGeographyEngineeringCivil engineering

Abstract

fetched live from OpenAlex

Abstract One of the current dominant strategies proposed for sustainable urban development is densification. While some advocate the very reasonable benefits of density, others emphasize the potential drawbacks. The main goal of this paper is to provide a systematic overview of the claimed benefits of densification in Swedish practice and relate this to the scientific evidence. For the systematic overview, comprehensive plans from 59 Swedish municipalities, covering plans from both highly urbanized areas as well as more rural regions, are included. The results show that in three out of four cases where density or densification is mentioned, no motive is given. For the other quarter, the most often used motivation is related to transport (19%), services (17%) and urban environmental qualities (14%). The least frequent motives used are related to health (8%) and ecology (2%). The motives in comprehensive plans are for the most part pointing to a positive impact of density on sustainable urban development (77%), which is not always supported by the empirical evidence that more often describe a negative correlation. Furthermore, many of the most frequently used motives in comprehensive plans have little scientific support, which puts new questions on the research agenda.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Systematic review · Consensus signal: Systematic review
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.329
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.045
GPT teacher head0.306
Teacher spread0.261 · 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 teacher head, not a consensus.

Study designSystematic review
Domainnot available
GenreReview

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

Citations11
Published2020
Admission routes1
Has abstractyes

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