MétaCan
Menu
Back to cohort
Record W3094624287 · doi:10.20961/shes.v3i1.45077

Adaptive Cycle Of The Wotsogo Village Community In Facing Covid-19 Pandemic In 2020

2020· article· en· W3094624287 on OpenAlexaff
Diah Ainurrohmah, Rita Noviani, Yasin Yusup

Bibliographic record

VenueSocial Humanities and Educational Studies (SHEs) Conference Series · 2020
Typearticle
Languageen
FieldSocial Sciences
TopicCOVID-19 Prevention and Impact
Canadian institutionsWiLAN (Canada)
Fundersnot available
KeywordsCoronavirus disease 2019 (COVID-19)PandemicPsychological resilienceMoment (physics)Resilience (materials science)Distribution (mathematics)Community resilience2019-20 coronavirus outbreakSocioeconomicsGeographyEconomic growthSociologyComputer sciencePsychologyEconomicsMathematicsBiologyMedicineVirologySocial psychologyDiseaseResource (disambiguation)

Abstract

fetched live from OpenAlex

<p><em>COVID-19 is non-natural disaster that influenced in various sector of life, not only health, but also social-economic conditions, including Wotsogo Village. Therefore, it is important to conduct research related to adaptive cycle of Wotsogo Village community in facing of COVID-19. This adaptive cycle can reflect the level of community resilience which influenced by various factors. This research used descriptive qualitative method with Miles and Huberman's interactive model and scoring technique analysis. The results of the analysis show that adaptive cycle of Wotsogo Village community in facing of COVID-19 was marked by three moments distribution, were the first distribution moment of COVID-19 in Indonesia, the second moment is implementation of COVID-19 policies, and last the towards New Normal moment. The scoring results show that the level of resilience of the Wotsogo Village community was classified as moderate that spread in three RW areas and high that spread in nine RW areas.</em></p>

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)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.146
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0020.002
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.276
GPT teacher head0.413
Teacher spread0.137 · 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 designQualitative
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
Published2020
Admission routes1
Has abstractyes

Explore more

Same venueSocial Humanities and Educational Studies (SHEs) Conference SeriesSame topicCOVID-19 Prevention and ImpactFrench-language works237,207