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3D real property in vertical mixed-use developments. A comparative analysis of common property and management aspects in selected jurisdictions – The case of British Columbia, Denmark and Sweden

2023· article· en· W4386761115 on OpenAlexaboutno aff
Morten Dalum Madsen, Jesper M. Paasch

Bibliographic record

VenueLand Use Policy · 2023
Typearticle
Languageen
FieldEngineering
Topic3D Modeling in Geospatial Applications
Canadian institutionsnot available
FundersInnovationsfonden
KeywordsLegislationJurisdictionProperty (philosophy)Property rightsProperty lawLaw and economicsCommon lawReal propertyBusinessLawPolitical scienceSociology

Abstract

fetched live from OpenAlex

Jurisdictions around the world are experiencing an increasing demand for formation of three-dimensional (3D) real property units, especially in the presence of vertical mixed-use developments. Since traditional real property formation is only in two dimensions (2D), jurisdictions are on different steps (levels) of transforming 2D real property legislation into 3D real property legislation. The objective of this article is to analyse and compare law and practice in jurisdictions that are on different steps of developing 3D real property legislation, but otherwise comparable in terms of societal development status. The predominant focus in academic literature is on formation of private 3D property rights. This paper has a somewhat different approach, focussing on formation and management of common property. The formation of common property is usually unavoidable in a vertical mixed-use development, e.g. due to a high degree of interdependence between layered and intertwined 3D property units. Legal aspects regarding common property formation and management have not been rigorously compared internationally. This article presents a comparative study including British Columbia (Canada), Denmark and Sweden. Each jurisdiction represents a unique step on the 3D transformation staircase where legal aspects regarding common property and management in each jurisdiction is analysed and presented. The results of the study can be beneficial for researchers and legislators as a tool to analyse 3D real property legislation.

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)0.001
metaresearch head score (Gemma)0.003
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.186
Threshold uncertainty score0.375

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0030.005
Science and technology studies0.0050.007
Scholarly communication0.0070.002
Open science0.0010.003
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0040.000

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 head0.027
GPT teacher head0.268
Teacher spread0.241 · 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 designQualitative
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

Citations4
Published2023
Admission routes1
Has abstractyes

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