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Record W4386384326 · doi:10.32435/envsmoke-2023-0011

THE AWARENESS AND UNDERSTANDING OF ENVIRONMENTAL DEGRADATION IN DHAKA (BANGLADESH) URBAN AREA: A GENDER PERSPECTIVE

2023· article· en· W4386384326 on OpenAlexaff
Pinki Shah, U. Sumaiya

Bibliographic record

VenueENVIRONMENTAL SMOKE · 2023
Typearticle
Languageen
FieldEnvironmental Science
TopicEnvironmental Education and Sustainability
Canadian institutionsUniversity of Windsor
Fundersnot available
KeywordsEnvironmental degradationPerspective (graphical)GeographyEnvironmental pollutionEnvironmental planningDiversity (politics)PopulationEconomic growthSocioeconomicsSustainable developmentPopulation growthEnvironmental protectionEnvironmental healthPolitical scienceSociologyEcologyMedicine

Abstract

fetched live from OpenAlex

Growth efforts and the growing population have been creating an adverse impact on our bio-diversity therefore human health is at higher risk, and human civilization came closer to a severe Environmental crisis. Impacts of environmental degradation especially of air and water pollution have been severe and challenging in Bangladesh, and generally, city dwellers face higher pollution levels than rural residents in their everyday lives. The broad aim of this study is to assess the environmental awareness level of the population of Dhaka city. Based on both primary and secondary data, the study reveals a huge lack of awareness of environmental challenges and the necessity of handling these concerns with appropriate initiatives. Gender perspective reveals women’s potential areas of contribution to handling environmental challenges. Both men and women need to be brought under strategic environmental awareness and education programs for sustainable and healthy urban life in Bangladesh, the study concludes.

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)0.001
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 score0.022
Threshold uncertainty score0.044

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0030.005
Scholarly communication0.0030.002
Open science0.0000.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0050.000

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 head0.035
GPT teacher head0.262
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

Citations0
Published2023
Admission routes1
Has abstractyes

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