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Record W2498746326 · doi:10.1057/9781137350800_11

Fertility Responses to Migrant Remittances in Pakistan

2014· book-chapter· en· W2498746326 on OpenAlexaff
Mazhar Mughal, Amar Anwar

Bibliographic record

VenuePalgrave Macmillan UK eBooks · 2014
Typebook-chapter
Languageen
FieldSocial Sciences
TopicMigration and Labor Dynamics
Canadian institutionsCape Breton University
Fundersnot available
KeywordsFertilityUrbanizationDemographic transitionEconomicsIndustrialisationHuman capitalDeveloping countryConsumption (sociology)Demographic economicsDevelopment economicsLabour economicsPopulationEconomic growthDemographySociologyMarket economy

Abstract

fetched live from OpenAlex

Migration and fertility are two of the main components of demographic dynamics. Both are individual or household-level responses to the chang- ing economic conditions that occur in urbanization industrialization, and higher returns to human capital accumulation. Migration is often in itself a household’s strategic response to the economic difficulties that high fertil- ity generates in an economy undergoing a transition. The two phenomena are thus closely associated, and hold a special significance in developing countries that experience demographic evolution from high-birth, high- mortality societies to low-fertility, low-mortality ones. In the developing countries, poor and irregular provision of healthcare and low penetration of the financial system means that migration can prove as a reliable strategy to cover risks related to household income and consumption (Massey et al., 1993). In particular, given its higher earning potential, international migra- tion can therefore be a means to better household health outcomes.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.026
Threshold uncertainty score0.051

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0050.000

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 head0.024
GPT teacher head0.311
Teacher spread0.287 · 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 source (direct Gemma or distilled Codex), 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

Citations2
Published2014
Admission routes1
Has abstractyes

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