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Record W2737614207 · doi:10.5539/jas.v9n8p174

The Use of Constant Market Share (CMS) Model to Assess Brazil Nut Market Competitiveness

2017· article· en· W2737614207 on OpenAlex

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.

venuePublished in a venue whose home country is Canada.
no affNo Canadian affiliation: this work is invisible to an affiliation-only frame.
No Canadian affiliation. An affiliation-only frame, the usual design, would never have seen this work. It is one of the works that make the case for inverting the frame.

Bibliographic record

VenueJournal of Agricultural Science · 2017
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicGlobal Trade and Competitiveness
Canadian institutionsnot available
FundersFundação da Universidade Federal do ParanáCoordenação de Aperfeiçoamento de Pessoal de Nível Superior
KeywordsMarket share analysisMarket shareNutFactor marketProduct (mathematics)BusinessEconomicsMarket concentrationMarket structureMarket microstructureIndustrial organizationMarket economyOrder (exchange)MarketingMathematics

Abstract

fetched live from OpenAlex

This paper aims to evaluate the variation of market share explained by structural and competitive forces using the Constant Market Share (CMS) model. Assuming that a country should maintain its market share to keep competitive, the equation used in the model analyzes the export basket composition, exports destination, growth or shrinkage of the world market and the competitiveness effect. The overall loss of the Brazilian market share in a time series from 1998-2012 is given due to the barriers of potential European markets and reduction of the market growth of the product with shell. In a different way, the increase in exports of shelled nuts to markets with higher growth rates contributed to a favorable outlook for Bolivian and Peruvian markets, which had a market share gain on the period.

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)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScholarly communication
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.506
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0020.004
Open science0.0020.000
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 head0.067
GPT teacher head0.275
Teacher spread0.208 · 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