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Record W4361279137 · doi:10.12924/cis2023.11010001

Evaluating the Effectiveness of Commercially Developed Appraisal Instruments (CDAIs) Using Composite Indices to Assess, Compare, and Rank the Liveability, Quality of Living and Sustainability Performance of Cities and Communities

2023· article· en· W4361279137 on OpenAlexaboutno aff
César A. Poveda

Bibliographic record

VenueChallenges in Sustainability · 2023
Typearticle
Languageen
FieldEngineering
TopicSustainable Building Design and Assessment
Canadian institutionsnot available
Fundersnot available
KeywordsWeightingRanking (information retrieval)Rank (graph theory)SustainabilityIdentification (biology)Relevance (law)Composite indexComputer scienceSelection (genetic algorithm)Management scienceComposite indicatorStatisticsData miningMathematicsEconometricsMachine learningEngineeringMedicine

Abstract

fetched live from OpenAlex

This manuscript presents an analysis of commercially developed appraisal instruments (CDAIs) using composite indices to assess, compare and rank the sustainability performance of cities and communities. A group of CDAIs using composite indices are commonly used to assess, compare, and rank the sustainability performance of cities and communities. As a sustainability assessment methodology, composite indices gather qualitative and quantitative information which is then used to calculate the overall performance of the principle (e.g., sustainability); the stand-alone number, commonly known as an index, is often used to compare and rank performance. Because of practicality and mistakenly perceived simplicity, the assessment methodology is often misunderstood and underestimated. Issues, skepticism, and criticism surrounding composite indices are rooted in the lack of structured and transparent methodological frameworks for the identification and selection of elements within each hierarchical level. Although scientifically-based methodologies and processes have been developed to assign relevance (i.e., weighting) and aggregate performance to calculate the stand-alone index, the effectiveness of the assessment methodology (i.e., composite indices) is still influenced by various degrees and types of subjectivity and uncertainty. To evaluate their effectiveness, the manuscript discusses three characteristics of CDAIs using composite indices: (1) the hierarchical structural organization (HSO) considers the aim of each hierarchical level in the assessment process, (2) the identification, selection and design of the elements (e.g., principle, sub-principles, criteria, indicators) included in each hierarchical level as a determinant factor in capturing the various facets of the sustainable development notion, and (3) the quantification methodology (i.e., weighting and aggregation system [W&AS]) implemented by the developer or proponent of the assessment tool. The analysis of CDAIs using composite indices effectiveness is partially assisted by three frameworks designed by consensus (FDC): (1) ISO 37130:2018 Sustainable development of communities—Indicators for city services and quality of life which is complemented with ISO 37122:2019 Sustainable cities and communities—Indicators for smart cities and ISO 37123:2019 Sustainable cities and communities—Indicators for resilient cities, (2) United Nations Sustainable Development Goals (UN SDGs) with emphasis on Goal 11, and (3) customized frameworks for sustainable cities (CFSS) with a focus on sustainability plans designed and implemented by the cities of Vancouver and Montreal which are used as case studies. While the findings support the applicability and usefulness of CDAIs using composite indices as assessment methodology, the appropriateness of comparing and ranking the sustainability performance of cities and communities is an unsettled debate with several areas for improvement and future research.

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)0.015
metaresearch head score (Gemma)0.003
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.121
Threshold uncertainty score0.904

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0150.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.001
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 head0.167
GPT teacher head0.415
Teacher spread0.248 · 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.

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

Citations4
Published2023
Admission routes1
Has abstractyes

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