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Record W4321460777 · doi:10.56527/fama.jabm.10.1.4

Malaysia's Chicken Shortage, A Solution Proposal Through Consumerism Advocacy

2023· article· en· W4321460777 on OpenAlexaboutno aff
Mohd Zulhemi Syafuddin Tan, Ahmad Naqiyuddin Bakar, Yarina Ahmad

Bibliographic record

VenueJournal of Agribusiness Marketing · 2023
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicFood and Agricultural Sciences
Canadian institutionsnot available
Fundersnot available
KeywordsBusinessChristian ministryGovernment (linguistics)Quarter (Canadian coin)Consumption (sociology)ConsumerismEconomic shortageAgricultural economicsMarketingAgricultural sciencePolitical scienceGeographyEconomicsBiology

Abstract

fetched live from OpenAlex

This study intended to observe Malaysia's chronic shortage of poultry eggs and meat, especially chicken, from September 2021 to July 2022. The pattern observed could answer the role every Malaysian can play through consumerism amid the current chicken crisis. A case interview is done by using McKinsey Case Interview Format. The corresponding author is currently a practitioner in the Policy and Strategic Planning Division, Ministry of Agriculture and Food Security (MAFS), Putrajaya. Data were obtained primarily through discussion with the two (2) senior officials of MAFS from their observation in several government strategic meetings. The scope of the study is limited to chicken eggs and meat. Poultry egg and meat undeniably are Malaysia's most sought source of protein as it is cheap and easily accessible. The shortage of poultry meat and egg supply caused social unrest. Malaysia's poultry egg self-sufficiency ratio is more than 100%, and almost 100% for poultry meat. However, Malaysia's per capita consumption is among the world's highest at 22.2 and 52.0 kg/person/year. If Malaysians diversify their protein intake and unintentionally create a more reasonable demand, despite all the disruptions to production, our current poultry supply should still be enough for everyone. This study suggested that Malaysians consume chicken more than they should chew and should be advocated to practice a healthier lifestyle. In guidelines by the Ministry of Health, i.e., 'Malaysia Healthy Plate: Quarter, Quarter, Half' and 'Malaysia Diet Guideline and Food Pyramid 2020', Malaysians were urged to eat more fiber, such as fruits or vegetables, and eat less protein, especially meat.

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)0.002
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.922
Threshold uncertainty score0.286

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.002
Science and technology studies0.0000.000
Scholarly communication0.0000.001
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 head0.018
GPT teacher head0.235
Teacher spread0.217 · 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

Citations2
Published2023
Admission routes1
Has abstractyes

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