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Record W4387789622 · doi:10.35772/ghm.2023.01058

Low fertility and fertility policies in the Asia-Pacific region

2023· review· en· W4387789622 on OpenAlex
Victoria Boydell, Rintaro Mori, Sadequa Shahrook, Stuart Gietel‐Basten

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 designOther design
Domainnot available
GenreReview

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".

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueGlobal Health & Medicine · 2023
Typereview
Languageen
FieldSocial Sciences
TopicDemographic Trends and Gender Preferences
Canadian institutionsHamilton Health Sciences
Fundersnot available
KeywordsFertilityWork (physics)Face (sociological concept)PopulationPolitical scienceEconomic growthDevelopment economicsEconomicsSociologyDemographySocial science

Abstract

fetched live from OpenAlex

Declining fertility is an increasing global trend. In many low fertility contexts, people are having fewer children then they want, and these unfulfilled fertility desires have been associated with wider socio-economic changes in education and labour force participation and conflicting and often contradictory expectations of women at home and at work. The right to determine if, when and how one has children is enshrined in international law yet many policies responses to low fertility fail to meet these standards. This paper summarizes why people in the Asia-Pacific region are having fewer children than they desire, and the range of policy responses, particularly those that make life easier for working parents. This raises two important points. First, we need to contend to the gender dynamics that underpin this in the region, despite gradual changes in women's roles, they are still seen as "caregivers" and undertake a disproportionate amount of unpaid care work, often having to lean-out of their employment, and/or face gender discrimination in the workplace. Second, the "emergency" of low fertility arises from complex social and economic conditions that cannot be solved by population policies solely focused on making babies.

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)0.005
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.976
Threshold uncertainty score0.964

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0050.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0020.000
Bibliometrics0.0000.002
Science and technology studies0.0010.001
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
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 head0.181
GPT teacher head0.472
Teacher spread0.291 · 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