ANALISIS YURIDIS TERHADAP PROGRAM PEMBANGUNAN FOOD ESTATE DI KAWASAN HUTAN DITINJAU DARI ECO-JUSTICE
Bibliographic record
Abstract
ABSTRAK Pada tahun 2020 Presiden Joko Widodo mengemukakan wacana pembangunan food estate sebagai respon atas peringatan krisis di masa pandemi Covid-19. Maka dari itu untuk memenuhi kebutuhan pangan dalam negeri, pemerintah menerbitkan Peraturan Menteri LHK No.24/2020 melalui Kementerian Lingkungan Hidup dan Kehutanan tentang Penyediaan Kawasan Hutan untuk Pembangunan Food Estate, yang kemudian peraturan tersebut dicabut dan digantikan dengan PermenLHK No. 7 Tahun 2021. Hasil penelitian menunjukkan bahwa kebijakan pembangunan food estate di kawasan hutan memiliki banyak problematika, yaitu bertentangan dengan peraturan yang lebih tinggi serta masalah dalam pengimplementasiannya. PermenLHK tersebut juga bertentangan dengan nilai-nilai keadilan ekologi di mana seharusnya manusia hidup berdampingan dengan harmonis bersama alam. Kata kunci: Kebijakan, Keadilan Ekologi, Lumbung Pangan, Penggunaan Lahan. ABSTRACT In 2020 President Joko Widodo announced a discourse on food estates as a response to the crisis warning during the Covid-19 pandemic. Therefore, to meet domestic food needs, the government issued Minister of Environment and Forestry Regulation No. 24/2020 through the Ministry of Environment and Forestry concerning Provision of Forest Areas for Food Estate Development, which was later revoked and replaced by PermenLHK No. 7 of 2021. The results show that the food estate development policy in forest areas has many problems, namely contrary to higher regulations and problems in its implementation. The PermenLHK also contradicts the values of ecological justice where humans should live side by side in harmony with nature. Keywords: Ecological Justice, Food Estate, Land Use, Policy.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 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) | 0.021 | 0.002 |
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".