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Record W3188656594 · doi:10.1079/9781789247053.0016

Gender mainstreaming in climate change adaptation strategies in Bangladesh and Nepal.

2021· book-chapter· en· W3188656594 on OpenAlexaff
S. Shehwar

Bibliographic record

VenueCABI eBooks · 2021
Typebook-chapter
Languageen
FieldSocial Sciences
TopicClimate Change, Adaptation, Migration
Canadian institutionsYork University
Fundersnot available
KeywordsGender mainstreamingMainstreamingClimate changePolitical scienceAdaptation (eye)GeographySouth asiaEconomic growthDevelopment economicsEnvironmental planningSocioeconomicsGender equalitySociologyGender studiesPsychologyEconomicsEcology

Abstract

fetched live from OpenAlex

This chapter discusses the state of gender mainstreaming in climate action activities and policies in two South Asian neighbors, Bangladesh and Nepal, based on a review of key climate change policy documents. Three questions are addressed: (1) How do Bangladesh and Nepal mitigate the detrimental effects of climate change for rural women? (2) How do climate policies and programmes in Bangladesh and Nepal respond to the different needs and concerns of these women within their national adaptation strategies? (3) What are the opportunities and challenges of mainstreaming gender in climate action policies and programmes in Bangladesh and Nepal? A key argument of this chapter is that climate change is not gender-neutral, and it has become increasingly necessary for Bangladesh and Nepal to learn from one another in order to build gender-sensitive strategies that are cognizant of the needs of rural women.

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)0.002
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: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.007
Threshold uncertainty score0.023

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0020.004
Scholarly communication0.0040.004
Open science0.0010.003
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0070.001

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 head0.180
GPT teacher head0.312
Teacher spread0.132 · 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
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

Citations0
Published2021
Admission routes1
Has abstractyes

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