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Record W4386562824 · doi:10.1177/00490857231187993

Empowered, Smaller Families Are Better for the Planet: How to Talk about Family Planning and Environmental Sustainability

2023· article· en· W4386562824 on OpenAlexaff
Céline Delacroix, Robert Engelman

Bibliographic record

VenueSocial Change · 2023
Typearticle
Languageen
FieldMedicine
TopicReproductive Health and Technologies
Canadian institutionsUniversity of Ottawa
Fundersnot available
KeywordsSustainabilityCognitive reframingEmpowermentPopulationNormativeEnvironmental degradationEnvironmental movementSociologySustainability organizationsPublic relationsEnvironmental ethicsEnvironmental resource managementPolitical scienceSocial psychologyPsychologyEcologyEconomicsLawBiology

Abstract

fetched live from OpenAlex

Despite their complex and nonlinear relationship, reproductive rights and environmental sustainability likely have a synergistic relationship. Social movement theory suggests that reframing reproductive rights in this light can strengthen and benefit them by diversifying their moral appeal and support base. Yet despite increasing scientific evidence demonstrating ways in which population size, growth, and distribution tend to undermine various aspects of environmental sustainability, and increasing public awareness and concern for environmental degradation, linking these issues remains a contested and polarised enterprise. In this article, we explore the marginalisation processes at play surrounding this linkage and introduce the concept of population reductionism. We review advice and normative trends on communicating messages linking the fulfilment of reproductive rights with improved environmental sustainability. We elaborate a strategic communication roadmap to promote the operationalisation of the family planning and environmental sustainability linkage, centred on individual empowerment, and propose a global rallying cry—‘empowered, smaller families are better for the planet’.

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)0.000
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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.189
Threshold uncertainty score0.344

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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)0.0000.000

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 head0.081
GPT teacher head0.330
Teacher spread0.249 · 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 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

Citations10
Published2023
Admission routes1
Has abstractyes

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