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Record W4404946058 · doi:10.1139/cjfas-2024-0117

Informal pricing in formal fish markets: evidence from the Solomon Islands

2024· article· en· W4404946058 on OpenAlexvenueno aff
Keisaku Higashida, Shoichi Kiyama, Kofi Otumawu-Apreku, Satoshi Yamazaki

Bibliographic record

VenueCanadian Journal of Fisheries and Aquatic Sciences · 2024
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicEconomics of Agriculture and Food Markets
Canadian institutionsnot available
FundersJapan Society for the Promotion of Science
KeywordsFisheryFish <Actinopterygii>FishingGeographyEconomicsBiology

Abstract

fetched live from OpenAlex

How are fish prices determined in the largest formal market in the Solomon Islands? The Honiara Central Market is the country’s primary fresh produce market, serving as an important conduit between rural food producers and urban consumers. One distinctive characteristic of this market is that despite a diverse range of reef fish species being available, all reef fish are traded nominally as homogeneous goods with no price differentiation. Using transaction-level data collected from the market, we show the prevalence of informal pricing, wherein the unit price of fish is implicitly differentiated based on fish species groups, quality, and buyers’ attributes. This result aligns with our expectation that diverse species groups and qualities are traded in a competitive environment with many vendors and buyers, where reef fish are indeed traded as heterogeneous products. Although fish provide a vital source of food and income for Pacific Island countries, the pricing of this vital natural resource is poorly understood. Our study provides new insights into how fish prices are determined in a formal market within the context of small island developing states.

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)0.009
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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.161
Threshold uncertainty score0.320

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.009
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.002
Scholarly communication0.0020.001
Open science0.0010.002
Research integrity0.0000.001
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 head0.026
GPT teacher head0.200
Teacher spread0.175 · 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
Published2024
Admission routes1
Has abstractyes

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