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Record W7145419594

貧困層の社会的包摂に取り組むスペインのマイクロクレジット

2011· article· ja· W7145419594 on OpenAlexaboutno aff
ひろみ 坪井, Hiromi Tsuboi

Bibliographic record

VenueInstitutional Repositories DataBase (IRDB) · 2011
Typearticle
Languageja
FieldEconomics, Econometrics and Finance
TopicCommunity Development and Social Impact
Canadian institutionsnot available
Fundersnot available
KeywordsUnemploymentFinancial institutionQuarter (Canadian coin)Focus groupInstitutionSocial assistanceImmigration
DOInot available

Abstract

fetched live from OpenAlex

The Spanish National Statistics Institute reported that the total number of residents in Spain as of January 1, 2011 was 47,150,819 inhabitants, and out of them 5,730,667 were foreign nationals, representing 12.2% of the total number registered. It also reported that in the first quarter of 2011, the number of unemployed persons stood up 4,910,200 and the unemployment rate reached 21.29% which was the highest in the European Union. Apart from the registered population, it is said that a large number of illegal immigrants live in Spain, looking for decent jobs. Under the current situation, a lot of microcredit institutions have provided the poor with small-uncollateralized loans to alleviate poverty. Their strategies are focused mainly on the limited financial support. In 2009, one microcredit institution launched upon a new project which organized the poor and adopted an integrated approach that both financial supports and non-financial ones were implemented. This paper examines how this project tries to include the poor among the community, with a focus on financial supports and non-financial ones. First, it provides a general overview of this project. Then, it describes the relationship between the project side and the project's group members. Finally, it suggests that this project is a social business which contributes to social inclusion.

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)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Science and technology studies, Insufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.866
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0020.001
Scholarly communication0.0000.002
Open science0.0010.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.002

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 head0.097
GPT teacher head0.250
Teacher spread0.154 · 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 designTheoretical or conceptual
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
Published2011
Admission routes1
Has abstractyes

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