Abandoned Land Utilization for Food Security: A Welfare-Based Policy Approach in Indonesia
Bibliographic record
Abstract
Concerning its land law system, Indonesia refers to Presidential Regulation No. 86 of 2018 on Agrarian Reform.As an organization of national power, the state must regulate the use of land solely for the prosperity of all components of the nation.Bringing benefit is the goal of Islamic law.Syatibi's Maqasid al-Shari'ah theory explained that God's main purpose in establishing the law is to bring benefits to human life.In this case, the law is linked to the management of abandoned lands and food resilience to achieve welfare.Land issues, especially those related to the availability of access to land and the utilization of wastelands, are crucial in Indonesia today because there are many abandoned lands that should produce food and increase the welfare of farmers.It is better if these abandoned lands are used to provide better food security.This research aims to describe the policy of abandoned land utilization and reformulate a welfare-based policy of abandoned land utilization management.This was normative legal research which used secondary data and library materials as the main data source.It used the statute and philosophical approaches.Research results showed that up to 2018, only 24,457,600 m 2 from 76,384,300 m 2 of abandoned lands were deemed clean and clear.There were 51,538,540 m 2 of abandoned lands that cannot function as supporters of agricultural productivity, although the optimization of these abandoned lands can help increase the currently depleting agricultural productivity in this country.Welfare-based policies in the utilization of abandoned lands in the context of food resilience is utilizing abandoned lands and giving them to farmers and stock farmers who have no land to bring benefit.This research on the policies and management in utilizing abandoned lands is crucial for the future to achieve the end goal of law application, namely bringing welfare to the Indonesian society.
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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 | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".