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Record W1883522681

Marriage, Motherhood and Masculinity in the Global Economy: Reconfigurations of Personal and Economic Life

2007· article· en· W1883522681 on OpenAlexfundno aff
Naila Kabeer

Bibliographic record

VenueLondon School of Economics and Political Science Research Online (London School of Economics and Political Science) · 2007
Typearticle
Languageen
FieldSocial Sciences
TopicGender, Labor, and Family Dynamics
Canadian institutionsnot available
FundersInternational Development Research Centre
KeywordsInjusticeEarningsUnpaid workGlobalizationMasculinityIdeologyPower (physics)Resistance (ecology)Work (physics)Political scienceGender studiesSociologyEconomicsMarket economyPolitics
DOInot available

Abstract

fetched live from OpenAlex

About IDSThe Institute of Development Studies is one of the world's leading organisations for research, teaching and communications on international development.Founded in 1966, the Institute enjoys an international reputation based on the quality of its work and the rigour with which it applies academic skills to real world challenges.Its purpose is to understand and explain the world, and to try to change it -to influence as well as to inform.IDS hosts five dynamic research programmes, five popular postgraduate courses, and a family of worldclass web-based knowledge services.These three spheres are integrated in a unique combination -as a development knowledge hub, IDS is connected into and is a convenor of networks throughout the world.The Institute is home to approximately 80 researchers, 50 knowledge services staff, 50 support staff and about 150 students at any one time.But the IDS community extends far beyond, encompassing an extensive network of partners, former staff and students across the development community worldwide.

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)0.001
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: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.004
Threshold uncertainty score0.011

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0020.009
Scholarly communication0.0030.002
Open science0.0000.002
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0030.000

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 head0.052
GPT teacher head0.375
Teacher spread0.323 · 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 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

Citations70
Published2007
Admission routes1
Has abstractyes

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