DISORGANISASI PENERAPAN PSBB DI DKI JAKARTA SEBAGAI REFLEKSI KOMPLEKSITAS RELASI PEMERINTAH PROVINSI DKI JAKARTA DAN PEMERINTAH PUSAT
Bibliographic record
Abstract
Large-scale social restrictions or PSBB have brought up immense multidimensional effects within the Indonesian political landscape. One of the things which could be dissected from this regard would be in the governance system. In this sense, one might take a look at the current management of the COVID-19 pandemic within the Province of Special Capital Region of Jakarta. In this province, the Indonesian central government has often „collided‟ with the province‟s regional government; which ranging from issues such as social assistance, transportation, economic, and also law enforcement. The impact of such „skirmish‟ has taken its toll on all of the city‟s residents; but most importantly hit it the hardest for the city‟s poorest and most vulnerable group. With this in mind, the goal of this paper is to broaden the knowledge within the issue of pandemic management, especially within the Indonesian context. Besides, it also strives to find out several recommendations which should be taken by the government in order to „solve‟ this issue as comprehensively as possible.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 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".