A Year of Covid-19: A Long Road to Recovery and Acceleration of Indonesia's Development
Bibliographic record
Abstract
2020 is the year of Covid-19, Indonesia feels the enormity of this pandemic in various aspects of development. The Indonesian economy during the year slowed down to minus 5.3 percent in the second quarter of 2020 and in aggregate growth was minus 2.1 percent in 2020. The target of development planning in the National Medium Term Development Plan (Rencana Pembangunan Jangka Menengah/RPJMN) 2020-2024 was revised through the updating of the Government Work Plan (Rencana Kerja Pemerintah/RKP) in 2020, with the main priority of overcoming Covid-19. Then development began to be intensified in 2021 to pursue national priority targets that were abandoned due to Covid-19. The 2020 State Budget allocates around IDR 937.42 trillion for the prevention of Covid-19, including the accumulated APBD (Regional Revenue and Expenditure Budget) IDR 86.32 trillion, which makes the deficit financing for that year reach IDR 1,226.8 trillion. The Covid-19 pandemic control policy through Large-Scale Social Restrictions Policy (Pembatasan Sosial Berskala Besar/PSBB) has had ups and downs, especially when coupled with the new normal policy. The Policy for Limiting Micro Community Activities (Pemberlakuan Pembatasan Kegiatan Masyarakat/PPKM) as a substitute for PSBB was implemented in early February and the parallel national vaccination program is expected to support accelerated development as outlined in the RKP 2021. In 2021, the Covid-19 pandemic is still high in the world, and the acceleration of development proclaimed by the government gets a stretch of road that extends to be traversed.
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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.008 | 0.004 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.006 |
| Insufficient payload (model declined to judge) | 0.020 | 0.007 |
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".