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Record W3094692085 · doi:10.22158/elp.v3n2p26

Assessing the Determinants of Food Security Status in Bangladesh: A Micro-Econometric Analysis

2020· article· en· W3094692085 on OpenAlex
Tithy Dev, Elias Hossain, Morteza Haghiri

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueEconomics Law and Policy · 2020
Typearticle
Languageen
FieldHealth Professions
TopicFood Security and Health in Diverse Populations
Canadian institutionsMemorial University of Newfoundland
Fundersnot available
KeywordsFood securityAgricultureLivestockAgricultural economicsFood processingProduction (economics)BusinessMalnutritionConsumption (sociology)Index (typography)SocioeconomicsEconomicsGeographyEconomic growthPolitical science

Abstract

fetched live from OpenAlex

Food security is an intricate issue which includes diverse aspects as well as many linkages. In Bangladesh, food security is tried to be achieved by increasing the production of rice both by employing modern agricultural technology as well as by increasing the area under rice production. Despite the impressive gains in increasing domestic food grain production, problems of food and nutrition security still remain. Bangladesh is yet to achieve comprehensive food security that resolves the problems of inadequate food intake and chronic malnutrition among those who are poor and vulnerable. The main objective of this paper is to the contribution of different factors behind household food security status of 180 households in three Northern districts of Bangladesh. The study area was chosen because relatively little energy consumption data are available concerning this geographical area. The study used both primary and secondary data. Food security status of each household was assessed on the basis of the food security line using the daily calorie intake recommended by FAO. This method has proven to be efficient in measuring food security at household level. Additionally, the use of a logistic regression model identified the factors that plays crucial role in determining the food security status of the households. Results from the food security index revealed that more than 60 percent of households were with food insecurity. In addition, we found that total monthly household income, age of household head, education level of household head, household size, farm size, gender of household head, livestock ownership and quantity of cereal production had significant influence on food security status at the household level.

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.

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 score0.187
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.150
GPT teacher head0.442
Teacher spread0.292 · 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