Legal Politics to Build a State of Happiness: An Idea in a State Based on the 1945 Constitution
Bibliographic record
Abstract
This research was conducted with the intent and purpose of answering two problem formulations, namely: what caused Indonesia to lag behind other countries, and secondly, how the legal politics in developing a country of happiness in Indonesia. The research method uses the method of legislation approach and comparison with literature studies. Based on research, several factors that cause Indonesia to lag behind other countries, namely: convoluted bureaucratic culture, a state administration that is not responsive to the needs of society, a national education system that is unable to produce quality graduates, unstable political conditions, Corruption, Collusion, and Nepotism are rife, and the development of primordial identity politics. Legal politics in developing a state of happiness is based on the purpose of a state based on the 1945 Constitution, namely: realizing a state of happiness. That is the idea of a state that must be realized in the life of the state in Indonesia which is mandated by the country's founders. Various efforts have been made in realizing this, but until now have not given satisfactory results. Indonesia is still far behind other countries. Of the 156 happiest countries chosen, Indonesia ranks 92. That means the government has not succeeded in giving happiness to its people.
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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 | 0.003 | 0.003 |
| 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 | 0.021 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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.
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".