New Model of Local Government Administrative Service in a New Normal Pattern of Behavior Era in Indonesia
Bibliographic record
Abstract
This study departs from a new habitual adaptation movement that changes people's social interactions as a rational choice amid the threat of the Covid-19 outbreak. The purpose of this article is to test empirically that the current model of local government administrative services in the archipelagic sub-districts is very inefficient and less productive so that it has an impact on meeting the basic needs of the community comfortably and fairly. Furthermore, this study offers a new model for Duo-TM, namely Temu Muka dan Temu Mesin. This article shows the need for an empirical study in the development of a local government administrative service model to trace geographic difficulties and risks due to the Covid-19 outbreak. This is especially relevant if the author refers to the reasons for the massive change in various social dimensions and forces the adaptation of new habits. On the other hand, the era of the 4.0 industrial revolution places the power of technology and information as a means for innovating services that are comfortable, fast, inexpensive, and of high quality.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".