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Record W3110815611 · doi:10.18311/jhsr/2020/25038

Study on Awareness about Food Adulteration and Consumer Rights among Consumers in Dhaka, Bangladesh

2020· article· en· W3110815611 on OpenAlexaff
Aishawarya Arefin, Paroma Arefin, Md Shehan Habib, Md Saidul Arefin

Bibliographic record

VenueJournal of Health Science Research · 2020
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicIdentification and Quantification in Food
Canadian institutionsInstitute of Health Economics
Fundersnot available
KeywordsBusinessGovernment (linguistics)MarketingFood safetyAttractivenessVariety (cybernetics)Consumer awarenessAdvertisingFood science

Abstract

fetched live from OpenAlex

Combating food adulteration is a great challenge in Bangladesh. The customer is the leading economic community and the focus of all commercial activities. The price, availability, variety, and attractiveness of consumer products have increased with the rise in people’s incomes. The business sector is practically bursting with complex technology-based new products. Regardless of misleading advertising, unsuitable media coverage, and food adulteration, it is challenging for the customer to select a particular foodstuff. The primary victim is a customer who casually takes adulterated food and pays the price of such corrupt practices. Bangladesh Government has enacted the Food Safety Act, 2013 to ensure food safety. But, consumer awareness is one of the most important steps towards reducing the adulteration of foods. Consumer faces different kinds of challenges to ensure food safety for them. To effectively face these challenges, consumers need to prepare themselves against these issues. In our present study, we have analyzed the different consumer behavior considering food adulteration and their awareness about consumer rights.

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 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)0.004
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 score0.063
Threshold uncertainty score0.304

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.000
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 head0.174
GPT teacher head0.462
Teacher spread0.288 · 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 teacher head, 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

Citations7
Published2020
Admission routes1
Has abstractyes

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