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Women Entrepreneurs in Conserving Land: An Analytical Study at the Sundarbans, Bangladesh

2012· article· en· W1522008565 on OpenAlexvenueno aff
Sajal Roy

Bibliographic record

VenueCanadian social science · 2012
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicAgricultural risk and resilience
Canadian institutionsnot available
Fundersnot available
KeywordsLivelihoodNatural disasterNatural resourceContext (archaeology)Environmental planningBusinessAccommodationEnvironmental resource managementEconomic growthPolitical scienceEnvironmental protectionGeographyNatural resource economicsAgricultureEconomics

Abstract

fetched live from OpenAlex

Women entrepreneurs as well organized and socially motivated group do contribute towards the protection of environmental resources. Land as important ingredients of natural environment provides not only livelihood but protects a greater mass during natural disaster. Cyclone Sidr and Ailla consecutively visited in 2007 and 2009 at the southern district Satkhira in Bangladesh. As a direct consequence of global climate change the incidents impelled ultra-poor people living in the costal belt to get lost their accommodation. Thereby in a post disaster context the agenda of environmental security through land conservation is one of the ways to protect the environment from being degraded. Women as agent of environmental development may contribute in this focused area. Accordingly this paper would like to spotlight different roles played by women entrepreneurs in land conservation with a view to ensuring environmental security in the disaster prone Gabura Union, Shamnagar, Satkhira. Key words: Women Entrepreneurs; Environmental resources; Gabura union

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)0.001
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.034
Threshold uncertainty score0.067

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0060.003
Scholarly communication0.0030.002
Open science0.0010.003
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0060.001

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 head0.022
GPT teacher head0.249
Teacher spread0.228 · 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 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

Citations9
Published2012
Admission routes1
Has abstractyes

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