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Economic Recovery Through Community Empowerment to Reduce Impact of the Covid-19

2021· article· en· W3179326071 on OpenAlexaboutno aff
Sukiyani Sukiyani

Bibliographic record

VenueProsperity Journal of Society and Empowerment · 2021
Typearticle
Languageen
FieldSocial Sciences
TopicSMEs Development and Digital Marketing
Canadian institutionsnot available
Fundersnot available
KeywordsEmpowermentQuarter (Canadian coin)Government (linguistics)PandemicCoronavirus disease 2019 (COVID-19)BusinessEconomic growthPublic relationsMarketingPolitical scienceGeographyEconomics

Abstract

fetched live from OpenAlex

The impact of the Covid-19 Pandemic Disaster is still very much felt by the economy of the people in the Special Region of Yogyakarta. The purpose of this research is to map the policies of the Provincial Government of the Special Region of Yogyakarta in empowering the community to the perpetrators of cooperative activities, micro, small and medium enterprises and to map the operational strategies carried out for the development of these policies. This study uses qualitative methods and uses secondary data from various kinds of literature such as books, articles, related journals regarding empowerment policies for actors in cooperative activities, micro, small and medium enterprises from the impact of the Covid-19 pandemic. The data analysis technique used in this research is descriptive analysis. The results of the research, it is known that the success of community empowerment policies for actors in cooperative activities, micro, small and medium enterprises in the Special Region of Yogyakarta from the impact of the Covid-19 pandemic disaster in the first quarter of 2021 experienced growth compared to the first quarter of 2020. The biggest contribution to economic growth in the Special Region of Yogyakarta in the first quarter of 2021 is the information and communication business field.

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)0.003
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation 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 score0.175
Threshold uncertainty score0.614

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
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 head0.042
GPT teacher head0.350
Teacher spread0.308 · 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.

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

Citations0
Published2021
Admission routes1
Has abstractyes

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Same venueProsperity Journal of Society and EmpowermentSame topicSMEs Development and Digital MarketingFrench-language works237,207