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Record W4200073781 · doi:10.1177/18681026211052052

Citizens’ Expectations for Crisis Management and the Involvement of Civil Society Organisations in China

2021· article· en· W4200073781 on OpenAlexafffund
Reza Hasmath, Timothy Hildebrandt, Jessica C. Teets, Jennifer Y.J. Hsu, Carolyn L. Hsu

Bibliographic record

VenueJournal of Current Chinese Affairs · 2021
Typearticle
Languageen
FieldSocial Sciences
TopicDisaster Management and Resilience
Canadian institutionsUniversity of Alberta
FundersNational University of SingaporeColgate UniversityUniversity of AlbertaLondon School of Economics and Political ScienceHarvard University
KeywordsCivil societyLegitimacyChinaCrisis managementPoliticsGovernment (linguistics)Public administrationPolitical scienceState (computer science)CommunismFunction (biology)Public relationsPolitical economySociologyLaw

Abstract

fetched live from OpenAlex

Chinese citizens are relatively happy with the state's management of national disasters and emergencies. However, they are increasingly concluding that the state alone cannot manage them. Leveraging the 2018 and 2020 Civic Participation in China Surveys, we find that more educated citizens conclude that the government has a leading role in crisis management, but there is ample room for civil society organisations (CSOs) to act in a complementary fashion. On a slightly diverging path, volunteers who have meaningfully interacted with CSOs are more skeptical than non-volunteers about CSOs’ organisational ability to fulfill this crisis management function. These findings imply that the political legitimacy of the Communist Party of China is not challenged by allowing CSOs a greater role in crisis management.

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)0.002
metaresearch head score (Gemma)0.004
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.039
Threshold uncertainty score0.077

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0020.001
Scholarly communication0.0020.001
Open science0.0000.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.000

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 head0.016
GPT teacher head0.309
Teacher spread0.292 · 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 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

Citations7
Published2021
Admission routes2
Has abstractyes

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