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Record W6888645548 · doi:10.21696/rcsl.v0n7.585

La configuración de la maternidad a través de la inversión social en los niños. Ejemplos de Canadá y México

2014· article· en· W6888645548 on OpenAlexaboutno aff

Bibliographic record

VenueRedalyc (Universidad Autónoma del Estado de México) · 2014
Typearticle
Languageen
FieldSocial Sciences
TopicFeminism, Gender, and Social Issues
Canadian institutionsnot available
Fundersnot available
KeywordsInvestment (military)Order (exchange)State (computer science)Social capitalHuman capitalRural areaCapital (architecture)

Abstract

fetched live from OpenAlex

As post-neoliberal scholars have pointed out, social investment targeting risk groups has be- come the focus of social spending by states in many countries around the world, and children, given their inherent potential, have increasingly become the focus of state investments aimed at enhancing their future integration into the market economy. This chapter details the results of two case studies which examine the regulation of motherhood in very different contexts where neo-liberal techniques target investment in children. In Canada recent campaigns have urged mothers to invest heavily in their children during their early years in order to enhance brain development and future market success. In Mexico, the state-sponsored anti-poverty program aims to increase children’s human capital development through the educational system as a way to ensure the future insertion of rural Mexicans into the market economy. The results of in-depth interviews with mothers in southern Ontario and rural Mexico are compared, and while the there are many class-based and cultural differences in the experiences of these mothers, there are also some surprising similarities in terms of the effects of a social investment framework on the regulation of motherhood.

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)0.003
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Science and technology studies, Research integrity
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.416
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.000
Meta-epidemiology (narrow)0.0000.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0010.002
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.015
GPT teacher head0.286
Teacher spread0.270 · 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.

Study designQualitative
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
Published2014
Admission routes1
Has abstractyes

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