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Record W4386410700 · doi:10.1108/pap-09-2023-147

Editorial: Policy analysis and practices on environmental issues, COVID-19 pandemic and more

2023· editorial· en· W4386410700 on OpenAlexfundno aff
Peter K.W. Fong

Bibliographic record

VenuePublic Administration and Policy · 2023
Typeeditorial
Languageen
FieldEnvironmental Science
TopicCOVID-19 impact on air quality
Canadian institutionsnot available
FundersUniversidade de MacauTongji UniversityYork University
KeywordsPandemicCoronavirus disease 2019 (COVID-19)2019-20 coronavirus outbreakSevere acute respiratory syndrome coronavirus 2 (SARS-CoV-2)Political scienceEnvironmental planningGeographyVirologyMedicineOutbreakInfectious disease (medical specialty)

Abstract

fetched live from OpenAlex

This second issue of 2023 consists of eight articles, with four related to the policy analysis and practices on environmental issues, two on the COVID-19 pandemic management, and two on problems on municipal and local government organizations.A brief introduction of these articles is given below.The first article on "California's cap-and-trade program: is it effective in advancing social, economic, and environmental equity?" by Alvin Holliman and Kimberly Collins investigates the effectiveness of the California cap-and-trade program.Key findings include evidence of enhancing social and environmental equity but ineffectiveness in reducing carbon emissions in accordance with state goals.The second article is "A comparative study of the adoption of public-private partnerships for water services in South Korea and Singapore" by Soojin Kim and Yujin Choi.The findings show that while South Korea has employed the PPP model mostly to expand and modernize their sewage systems, Singapore has used PPPs to build and operate desalination facilities that produce potable water through the treatment and filtration of wastewater.The third article is "Grassroots involvement on Global South policy narratives and deliberative action on climate change loss and damage: a case in the Philippines" by Noe John Sacramento.Based on the case of a community-driven workshop in Bohol, Philippines, it highlights the importance of hearing grassroots voices and the need to balance technocratic and critical approaches to policy analysis.

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)0.002
metaresearch head score (Gemma)0.019
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Meta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Editorial · Consensus signal: Editorial
Teacher disagreement score0.211
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.019
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0000.000
Research integrity0.0010.001
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 head0.053
GPT teacher head0.436
Teacher spread0.383 · 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 designNot applicable
Domainnot available
GenreEditorial

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
Published2023
Admission routes1
Has abstractyes

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