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Record W4352988443 · doi:10.58823/jham.v13i13.103

HAM dan Pemerintah Daerah: Ikhtiar Membumikan HAM di Level Lokal

2021· article· id· W4352988443 on OpenAlexaff
Asep Mulyana

Bibliographic record

VenueJurnal Hak Asasi Manusia · 2021
Typearticle
Languageid
FieldSocial Sciences
TopicLegal and Policy Analysis in Indonesia
Canadian institutionsEncana (Canada)
Fundersnot available
KeywordsPolitical scienceHumanitiesArt

Abstract

fetched live from OpenAlex

Wacana tentang pemerintah daerah dan Hak Asasi Manusia (HAM) baru-baru ini menjadi topik penting dalam pertemuan-pertemuan HAM internasional. Wacana itu didorong oleh kebutuhan untuk mengimplementasikan norma dan standar HAM ke dalam praktik langsung di tingkat lokal. HAM yang sudah diakui secara internasional memiliki persoalan di tingkat pelaksanaan. Pelanggaran HAM kerap terjadi di level lokal. Memutus mata rantai pelanggaran HAM dipandang akan lebih efektif jika pada level lokal dibangun kapasitas pemerintah dalam menunaikan kewajiban HAM. Penghormatan, pemenuhan, dan perlindungan HAM akan lebih terasa dampaknya bagi masyarakat, terutama kelompok rentan jika otoritas di level lokal didorong untuk memiliki kesadaran HAM dan, dengan itu, mampu menyusun pilihan-pilihan kebijakan dan membangun praktik- praktik terbaik bagi perwujudan penikmatan HAM di tingkat lokal. Program Kota HAM adalah salah satu upaya yang diarahkan untuk membangun kapasitas pemerintah daerah dalam penegakan HAM. Program ini juga harus melibatkan sejauh mungkin partisipasi dan kapasitas politik masyarakat, sehingga Program Kota HAM menjadi milik dan dipelihara oleh publik.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.026
Threshold uncertainty score0.086

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0020.002
Scholarly communication0.0050.003
Open science0.0010.002
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0260.007

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 head0.058
GPT teacher head0.325
Teacher spread0.267 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
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

Citations4
Published2021
Admission routes1
Has abstractyes

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