Media Vs. Law: Which Acts as a Tool of Social Engineering?
Bibliographic record
Abstract
According to Roscoe Pound, law is viewed as a tool for social engineering. However, the situation in Indonesia reveals that the law has not effectively fulfilled its role as a tool for social engineering and development, as envisioned by Mochtar Kusumaatmadja. This is evident in various law enforcement cases in Indonesia, where the process tends to be sluggish and only gains attention after it becomes viral in the mass media. This study aims to explore the underlying factors behind the influence of mass media on law enforcement in Indonesia and investigate whether both the media and the law can function as tools for social engineering simultaneously. The article adopts a normative legal research methodology, utilizing statutory, conceptual, and case-based approaches. The research findings demonstrate that while the mass media has a positive impact, there are still areas for improvement within the Indonesian legal system, particularly concerning the suboptimal performance of law enforcement officials and state authorities. Despite the potential for mutual support between the media and the law, the current scenario highlights the need for the media to serve as an information disseminator, supervisor, social control, and shaper of public opinion, while the coercive nature of the law can exert pressure on law enforcers and government officials to fulfill their duties and responsibilities.
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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 | 0.008 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.003 | 0.021 |
| Scholarly communication | 0.014 | 0.014 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".