MétaCan
Menu
Back to cohort
Record W2139646085 · doi:10.5539/ass.v8n11p107

Need-Based Street Children Management in Surakarta City of Central Java Province of Indonesia

2012· article· en· W2139646085 on OpenAlexvenueno aff
Argyo Demartoto

Bibliographic record

VenueAsian Social Science · 2012
Typearticle
Languageen
FieldHealth Professions
TopicHomelessness and Social Issues
Canadian institutionsnot available
Fundersnot available
KeywordsPovertySocializationWork (physics)Variety (cybernetics)SociologySocioeconomicsPsychologyEconomic growthSocial scienceEngineering

Abstract

fetched live from OpenAlex

The objective of research is to find out the characteristics of street children, the cause of children becoming street children, and the management of street children problems in Surakarta City. As the marginalized and alienated children from the hard environment of city, the some street children living and working in the street, work in the street but still return back to their parents’ home everyday, then some of them work in the street and return back to their origin once in 1 – 3 months and the problematic street adolescents disperse in a variety of certain zones because of poverty, domestic violence, parents’ encouragement, and children’s environmental factor. The management of street children is determined by the need and problem the street children is facing whether using street based with street literacy, centre based and re-socialization through the open house for street children as well as community based approaches by conducting activity and advocacy on the street children problems involving all potencies of society. In fact, the approaches above are overlapping. The most important point is our empathy and commitment to manager the street children problem.

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 score0.048
Threshold uncertainty score0.413

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.001
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 head0.029
GPT teacher head0.368
Teacher spread0.338 · 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

Citations17
Published2012
Admission routes1
Has abstractyes

Explore more

Same venueAsian Social ScienceSame topicHomelessness and Social IssuesFrench-language works237,207