Potensi Penerapan Eco-City untuk Mitigasi Pandemi di Kota-Kota di Indonesia pada Masa Depan
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".