MétaCan
Menu
Back to cohort

Pengaruh Perekonomian Indonesia di Berbagai Sektor Akibat Corona Virus Disease 2019 (Covid-19)

2020· article· en· W3085337225 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.

aboutThe title or abstract carries a Canadian signal from the geographic lexicon.
no affNo Canadian affiliation: this work is invisible to an affiliation-only frame.
No Canadian affiliation. An affiliation-only frame, the usual design, would never have seen this work. It is one of the works that make the case for inverting the frame.

Bibliographic record

VenueWidya Akuntansi dan Keuangan · 2020
Typearticle
Languageen
FieldSocial Sciences
TopicSMEs Development and Digital Marketing
Canadian institutionsnot available
Fundersnot available
KeywordsQuarter (Canadian coin)Coronavirus disease 2019 (COVID-19)PandemicOutbreakBusinessGeographyVirologyInfectious disease (medical specialty)DiseaseMedicine

Abstract

fetched live from OpenAlex

Pengaruh Perekonomian Indonesia di Berbagai Sektor Akibat Corona Virus Disease 2019 (Covid-19). This research was carried out based on the covid-19 outbreak that is currently happening in Indonesia and in several countries. This pandemic is not only threatening public health and safety but also threatens the economic situation in various sectors. The research method used in this research is qualitative research, data collection techniques used in this study are library research techniques. Based on the results of the Bank Indonesia (BI) Business Activity Survey (SKDU) in Quarter I-2020 indicating a decline in the economy in various sectors. This can be seen from the Weighted Net Balance (WNB) value in the first quarter of 2020 amounting to -5.56%, which is quite deep compared to 7.79% in quarter IV-2019. The decrease was caused by the decrease in demand and supply due to co-19.

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)0.001
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.507
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.000
Scholarly communication0.0000.001
Open science0.0010.000
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 head0.040
GPT teacher head0.296
Teacher spread0.256 · 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