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Record W3185115016

Economy and Human Rights: Impact During A Pandemic and Economic Recovery

2021· article· en· W3185115016 on OpenAlexaboutno aff
Felenia Marcelitha, Hadi Wijaya, Priseila Vania Maharani, Hamdan Mustameer

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldSocial Sciences
TopicSMEs Development and Digital Marketing
Canadian institutionsnot available
Fundersnot available
KeywordsQuarter (Canadian coin)Gross domestic productRecessionEconomic recoveryEconomic sectorEconomyPandemicCoronavirus disease 2019 (COVID-19)BusinessEconomicsEconomic growthGeographyMacroeconomics
DOInot available

Abstract

fetched live from OpenAlex

The Covid-19 pandemic has hit the world, and it affects various sectors, especially the economic sector. The Badan Pusat Statistik(BPS) shows that Indonesia's economic growth has decreased. In the first quarter of 2020, economic growth was only 2.97%. This growth has reduced when compared to economic growth in the same quarter in 2019. Data from the Badan Pusat Statistik on August 5, 2020 states that the decline in Indonesia's economy in the second quarter of 2020 was -5.32%. The methodology used in this research is the literature approach, which utilizes various literature as data sources. This research shows that the economic downturn in Indonesia is that many employees have been disconnected from their company, and there are still creative economy businesses that are still running. When examined, the reason the company terminated its employees' employment relationship was force majeure. Meanwhile, the company has not fulfilled the requirements to complete the working relationship. Creative economy businesses can restore the economy in Indonesia; this is based on a joint survey by the Badan Ekonomi Kreatif and the Badan Pusat Statistik in 2016. Creative economy contributes to growing the economy by 7.44% in Gross Domestic Product (GDP).

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)0.000
metaresearch head score (Gemma)0.000
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 score0.340
Threshold uncertainty score0.582

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.282
Teacher spread0.266 · 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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