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Record W4408613271 · doi:10.3390/sexes6010013

Regarding the UN Sustainable Goals of Well-Being, Gender Equality, and Climate Action: Reconsidering Reproductive Expectations of Women Worldwide

2025· article· en· W4408613271 on OpenAlexaff
Carol Nash

Bibliographic record

VenueSexes · 2025
Typearticle
Languageen
FieldHealth Professions
TopicGlobal Health Care Issues
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsGender equalityAction (physics)Climate changePolitical scienceSustainable developmentPsychologyGender studiesSociologyEcologyBiologyLaw

Abstract

fetched live from OpenAlex

Climate action represents the most comprehensive of the 2015 United Nations 17 Sustainable Development Goals (SDGs) in that climate change impacts all other goals. Urban overpopulation is a primary cause, as energy consumption is a significant source of carbon dioxide emissions directing climate change. The population increase origin is attributable to the agricultural/urban developments that became geographically widespread approximately 6000 years ago. Simultaneously, religious belief stressed multiple children, with women obligated to produce them. This female duty created gender inequality and reduced the health and well-being of women, as pregnancy is a noted risk factor for decreased lifetime health. Regardless of the detrimental risk to their health and well-being, the gender inequality, and the adverse effects of birthing multiple children regarding climate action, women today continue to feel obliged to reproduce appropriately. This burden requires change to meet the three sustainable development goals of good health and well-being (SDG 3), gender equality (SDG 5), and climate action (SDG 13). An author-developed mindfulness-based psychoanalytic narrative research method presents a means for promoting such change based on a qualitative narrative analysis of the responses of several participants regarding its success in clarifying the values of these women in overcoming career-related burnout.

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)0.002
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation 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 score0.486
Threshold uncertainty score0.621

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
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 head0.064
GPT teacher head0.425
Teacher spread0.361 · 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 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

Citations1
Published2025
Admission routes1
Has abstractyes

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