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Record W2131912368 · doi:10.1093/pubmed/fdq066

The empowerment of women and the population dynamics of climate change

2010· letter· en· W2131912368 on OpenAlexaff
Ashley Page, Martin J. Larsen

Bibliographic record

VenueJournal of Public Health · 2010
Typeletter
Languageen
FieldSocial Sciences
TopicClimate Change, Adaptation, Migration
Canadian institutionsUniversity of Ottawa
Fundersnot available
KeywordsClimate changeEmpowermentDynamics (music)PopulationPublic healthEnvironmental healthGeographyMedicinePsychologyEconomic growthEcologyEconomicsBiology

Abstract

fetched live from OpenAlex

Dear editor, We welcome the focus on climate change and population growth, raised by Stephenson et al.1 Particularly in the context of reproductive health we agree with the sentiment that rapid population growth jeopardizes both human development and the capacity of poor communities to adapt to climate change is generally well known. While Stephenson et al.1 have ably addressed several key issues related to climate change and human health, the potential for women as agents of change has been somewhat overlooked. Specifically while reproductive health is discussed as a means to combat rapid population growth, the significance of empowering women on a global level is omitted. Women can be effective agents of change when addressing global climate change adaptation strategies. To effectively address rapid population growth, a culturally sensitive model for women's reproductive health services and education is required. Evidence confirms that such services result in the empowerment of women, and fewer births.2 Additionally, ‘the lack of access to reproductive health services undermines achievement of most if not all of the Millennium Development Goals (MDG)'.3 However, reproductive health is still not readily employed as a climate change strategy despite reports indicating that the provision of reproductive health services is the most cost-effective climate change strategy.4 Therefore, the empowerment of women through the provision of reproductive health services acts as both a adaptation strategy, and a mitigation strategy, each of which are highlighted by Stephenson et al.1 to tackle the challenges of climate change. A important outcome of the June 2010 G8 meeting in Toronto, Canada was the Muskoka Declaration, which called for a renewed emphasis on maternal and reproductive health. The declaration explicitly states that progress towards maternal and reproductive health has been unacceptably slow. To that end, global leaders have pledged $5 billion USD to be delivered through the Muskoka initiative, to accelerate MDGs 4 and 5, which focus specifically on child and maternal health. Additionally, issues of climate change in developing countries are addressed with the G8 making a commitment to share national strategies and experiences. Although this new commitment provides an opportunity to accomplish the goals of healthy outcomes for the world population and for the planet, it is disappointing that reproductive health and climate change remain unlinked. In light of the declaration, we agree with Stephenson et al. assertions that ‘population dynamics have not been integrated systematically into climate change science' and that ‘the contribution of population growth … to mitigation and adaptation programmes needs urgent investigation.' The links between women's health, population outcomes and climate wellbeing have been established. The time has come to integrate the sexual and reproductive health community into the climate change agenda in order to actualize change that will benefit women, nations and the environment.

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)0.004
metaresearch head score (Gemma)0.018
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Commentary · Consensus signal: Commentary
Teacher disagreement score0.032
Threshold uncertainty score0.026

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.018
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.000
Science and technology studies0.0050.006
Scholarly communication0.0040.006
Open science0.0010.003
Research integrity0.0320.035
Insufficient payload (model declined to judge)0.0080.001

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 head0.124
GPT teacher head0.366
Teacher spread0.242 · 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 designNot applicable
Domainnot available
GenreCommentary

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

Citations4
Published2010
Admission routes1
Has abstractno

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