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Record W7120368628

O Programa Social Federal Id Jovem no município de Nova Friburgo/RJ: possibilidades de inclusão da população de baixa renda nas mobilidades de lazer

2023· dissertation· pt· W7120368628 on OpenAlexaboutno aff
Kevin Rigotti Prestes

Bibliographic record

VenueLA Referencia (Red Federada de Repositorios Institucionales de Publicaciones Científicas) · 2023
Typedissertation
Languagept
FieldSocial Sciences
TopicYouth, Politics, and Society
Canadian institutionsnot available
Fundersnot available
KeywordsContext (archaeology)GovernoGovernment (linguistics)Social assistanceNova scotia
DOInot available

Abstract

fetched live from OpenAlex

Esta pesquisa teve como objetivo principal avaliar como vem sendo executado o Programa Social Federal Id Jovem no município de Nova Friburgo. Para tanto, definiu-se a metodologia composta por revisão de literatura, pesquisa documental, levantamento de dados secundários em sites oficiais do governo federal, entrevistas com roteiro semi-estruturado com atores ligados ao Sistema Municipal de Assistência Social, abordagem de empresa de transporte interestadual utilizando a técnica de “Cliente Oculto” e o acompanhamento de uma jovem em viagem utilizando a técnica de “sombreamento”, de caráter qualitativo e base etnográfica. Complementando o escopo metodológico, optou-se pelo método da História Oral para a construção e análise das entrevistas com roteiro semi-estruturado. Os resultados obtidos apontam que o Programa Social Federal Id Jovem ainda não promove no município de Nova Friburgo/RJ a inclusão da população jovem de baixa renda, de forma satisfatória, nas mobilidades de lazer. A adesão de jovens ao programa é “insignificante” perante o seu potencial. Além disso, diversas outras barreiras de ordem econômica, social e política dificultam ainda mais o acesso dos jovens a este benefício.

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)0.005
metaresearch head score (Gemma)0.004
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Science and technology studies, Scholarly communication, Research integrity
Consensus categoriesMeta-epidemiology (narrow), Research integrity
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.703
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0050.004
Meta-epidemiology (narrow)0.0020.002
Meta-epidemiology (broad)0.0020.002
Bibliometrics0.0010.002
Science and technology studies0.0090.000
Scholarly communication0.0040.001
Open science0.0030.001
Research integrity0.0050.004
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 head0.046
GPT teacher head0.320
Teacher spread0.273 · 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; both teacher heads agree on what is shown here.

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

Citations0
Published2023
Admission routes1
Has abstractyes

Explore more

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