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Record W4386549452 · doi:10.14710/jwl.10.3.282-296

Potensi Penerapan Eco-City untuk Mitigasi Pandemi di Kota-Kota di Indonesia pada Masa Depan

2022· article· id· W4386549452 on OpenAlexaff
Valendya Rilansari, Chrisna Trie Hadi Permana

Bibliographic record

VenueJurnal Wilayah dan Lingkungan · 2022
Typearticle
Languageid
FieldSocial Sciences
TopicCOVID-19 Prevention and Impact
Canadian institutionsEncana (Canada)
Fundersnot available
KeywordsScope (computer science)Context (archaeology)ScopusLimitingEnvironmental planningPolitical scienceIndonesianGeographyEnvironmental resource managementEngineeringComputer scienceEnvironmental science

Abstract

fetched live from OpenAlex

The Covid-19 pandemic that is plagued worldwide, has prompted research from various fields to develop strategies to mitigate similar events in the future. In the context of urban planning, the concept of an Eco-City or ecology-based city is one of the concepts offered by the literature trend that has developed in recent times. This concept discusses the development and arrangement of cities by planning to ensure ecosystem balance. This research aims to do a literature review that systematically discusses the theories and policies of eco-cities implemented in various countries in Asia using a systematic review method. The stages of a systematic review of this research include: a) limiting the scope and definition of the problem with a scopus indexed, b) selection of search results for cities in Asia, and c) screening research based on the citation score and impact factor. In this study, the concept of Eco-City is discussed in relation to the ability of a city to mitigate pandemics that may recur in the future. This study summarizes all findings, facts, and dynamics presented in the literature referenced using Microsoft Excel and Nvivo12. Finally, the findings of this study are discussed in the context of their potential application in Indonesian cities, which are divided into five aspects: social, environmental, economic, infrastructure, and governance.

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)0.003
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Science and technology studies, Research integrity
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.480
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.001
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.002
Science and technology studies0.0060.001
Scholarly communication0.0010.001
Open science0.0020.001
Research integrity0.0000.003
Insufficient payload (model declined to judge)0.0010.000

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 head0.031
GPT teacher head0.311
Teacher spread0.280 · 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 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

Citations0
Published2022
Admission routes1
Has abstractyes

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