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Record W2097239664 · doi:10.1002/pop4.107

A Retrospective Look at How Well a Macro‐Micro Model Can Analyze the Impact of the Global Financial Crisis on a Developing Country

2015· article· en· W2097239664 on OpenAlexaff
Margaret Chitiga, Bernard Decaluwé, Ramos Mabugu, Hélène Maisonnave, Véronique Robichaud, Debra Shepherd, Servaas van der Berg, Dieter von Fintel

Bibliographic record

VenuePoverty & Public Policy · 2015
Typearticle
Languageen
FieldSocial Sciences
TopicIncome, Poverty, and Inequality
Canadian institutionsUniversité Laval
Fundersnot available
KeywordsMacroFinancial crisisPovertyPosition (finance)Context (archaeology)Micro levelEconomicsMacro levelDevelopment economicsMacroeconomicsEconomic growthEconomic impact analysisComputer scienceFinanceGeography

Abstract

fetched live from OpenAlex

This article is a follow‐up to one that looked at the impact of the economic crisis on child poverty in South Africa using a macro–micro modeling technique. The current article's contribution is to position the results of the first article in the context of the actual effects of the crisis. Hence, this article offers a comparative analysis between the results of the modeling with the actual observed outcomes. It also offers explanations for why some model results are different from what obtained in the country after the crisis period.

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)0.004
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.491
Threshold uncertainty score0.997

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.002
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.041
GPT teacher head0.334
Teacher spread0.294 · 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

Citations0
Published2015
Admission routes1
Has abstractyes

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