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PENGARUH BANTUAN PANGAN NON TUNAI TERHADAP TINGKAT KEMISKINAN DI KOTA MAGELANG MELALUI ANALISIS SIMULASI

2022· article· en· W4293213150 on OpenAlex

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.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueJurnal Jendela Inovasi Daerah · 2022
Typearticle
Languageen
FieldSocial Sciences
TopicLocal Governance and Development
Canadian institutionsEncana (Canada)
Fundersnot available
KeywordsPovertyCashPoverty reductionPer capitaBusinessPoor peopleGovernment (linguistics)EconomicsEconomic growthEnvironmental healthFinance

Abstract

fetched live from OpenAlex

Poverty is a classic and interesting problem to study. Poverty is a multidimensional problem and has broad impact. Poverty can be seen as inability to fulfil various needs, shackled of people by tradition or culture factors, or helplessness of people against an unfair social system. The handling of poverty needs comprehensive policies from all related parties. The government is one of the keys to success of poverty alleviation programs through programs provided. One of them is Non-Cash Food Assistance. This research wants to study the effect of Non-Cash Food Assistance on poverty in Magelang City. The data used is expenditure per capita form March Susenas data from 2019 to 2021. The results of the analysis show that Non-Cash Food Assistance has effects on poverty reduction in Magelang City.

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 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)0.002
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Science and technology studies, Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.712
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0020.000
Scholarly communication0.0000.001
Open science0.0010.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.019
GPT teacher head0.280
Teacher spread0.261 · 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