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Record W3216186510 · doi:10.1080/17516234.2021.2007565

Fighting Covid-19 in rural communities: coordinated mobilization and reconstruction of community order in a village in Northern China

2021· article· en· W3216186510 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueJournal of Asian Public Policy · 2021
Typearticle
Languageen
FieldSocial Sciences
TopicSoutheast Asian Sociopolitical Studies
Canadian institutionsUniversity of Toronto
FundersGovernment of Inner Mongolia Autonomous Region
KeywordsGrassrootsChinaCoronavirus disease 2019 (COVID-19)PandemicState (computer science)Order (exchange)MobilizationPolitical scienceState of emergencyFace (sociological concept)Power (physics)Emergency managementEconomic growthSocial mobilization2019-20 coronavirus outbreakGeographyBusinessSociologyMedicineLawPoliticsEconomics

Abstract

fetched live from OpenAlex

Taking a remote village in Inner Mongolia Autonomous Region as a case study, this paper discusses how coordinated mobilization constructed a temporary grassroots-level emergency order in response to the Covid-19 pandemic. The study reveals that the temporary emergency order was established through a combination of state power, villagers' understanding of the infection risks of the coronavirus, and village self-management traditions. It finds that party members, elites, and villagers made a coordinated effort to mobilize and fight Covid-19. The paper concludes the state can effectively mobilize loosely-knit rural communities to face major risks such as the Covid-19 pandemic.

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.

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)0.007
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.414
Threshold uncertainty score0.948

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.007
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.032
GPT teacher head0.337
Teacher spread0.305 · 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