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Record W4388014973 · doi:10.18280/ijsdp.181031

The Utilization of Abandoned Land in Indonesia: A Comparative Study on Malaysia in the Fulfillment of Human Rights

2023· article· en· W4388014973 on OpenAlexvenueno aff
Rahayu Subekti, Albertus Usada

Bibliographic record

VenueInternational Journal of Sustainable Development and Planning · 2023
Typearticle
Languageen
FieldSocial Sciences
TopicLegal Studies and Policies
Canadian institutionsnot available
Fundersnot available
KeywordsHuman rightsBusinessEnvironmental planningSocioeconomicsNatural resource economicsGeographyEnvironmental protectionPolitical scienceEconomicsLaw

Abstract

fetched live from OpenAlex

Indonesia has established regulations on the location of Abandoned Land Utilization.However, the important question is whether the regulation is effective and whether the utilization has become a means of fulfilling human rights over land.This study aims to analyze the utilization of abandoned land that is an effort to fulfill human rights over land.This study is a normative legal study.It used secondary data with primary, secondary, and tertiary legal materials.It covers the mechanisms and procedures to eliminate and to utilize abandoned land, its social function, as well as the relationship and relevance between fulfillment of human rights to land and the utilization of abandoned land.It also compared the similar case to Malaysian case.The results show that the state must make efficient use of abandoned land and prioritize the principle of social function rather than to make land as merely an economic commodity owned by certain groups.Moreover, the amount of land is fixed but the demand continues to increase.Therefore, the efforts to utilize abandoned land are a progressive solution because the goal of agrarian reform is for equity and social justice, especially for the poor.The result of research shows that the regulation of abandoned land is carried out using Law and the land that has been determined as the abandoned one will be taken over by the state using simple mechanism and in Malaysia vertical attachment principle is used.

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.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.126
Threshold uncertainty score0.207

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.063
GPT teacher head0.372
Teacher spread0.309 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2023
Admission routes1
Has abstractyes

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