Economy and Human Rights: Impact During A Pandemic and Economic Recovery
Bibliographic record
Abstract
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 machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.006 |
| Scholarly communication | 0.007 | 0.008 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.012 | 0.001 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".