ANALISIS YURIDIS TERHADAP PROGRAM PEMBANGUNAN FOOD ESTATE DI KAWASAN HUTAN DITINJAU DARI ECO-JUSTICE
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.000 | 0.003 |
| Insufficient payload (model declined to judge) | 0.001 | 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 it