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Record W2161008114 · doi:10.1108/13598540410550064

Managing the value of new‐trait varieties in the canola supply chain in Canada

2004· article· en· W2161008114 on OpenAlexaffabout
Peter W.B. Phillips, Stuart J. Smyth

Bibliographic record

VenueSupply Chain Management An International Journal · 2004
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicCooperative Studies and Economics
Canadian institutionsUniversity of Saskatchewan
Fundersnot available
KeywordsTransaction costProduction (economics)Industrial organizationBusinessAsset specificityCanolaAsset (computer security)Supply chainTraitAgency (philosophy)Corporate governanceMarketingMicroeconomicsEconomicsComputer scienceFinance

Abstract

fetched live from OpenAlex

Identifies the drivers, classifies the structures, examines the governance systems and estimates the relative economic costs and benefits of various identity‐preserved production and marketing (IPPM) systems that have evolved in the Canadian canola industry. The systems vary significantly, depending on whether they are managing input‐ or output‐based, traditionally bred or biotechnology‐based traits. Combines transaction costs and principal‐agent theory in a synthesized transaction cost‐agency model that allows for predictions regarding the organizational form of vertical integration based on the degree of asset specificity, task programmability and non‐separability. Transactions for new, proprietary, novel‐trait canola varieties require a more extensive set of institutions than traditional varieties. Identity‐preserved production and marketing systems appear technically feasible for smaller units of production, but it is unclear whether they are economically viable for long‐term or larger‐scale operations. IPPM systems can provide an effective and proven method of controlling risks and liabilities.

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)0.001
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 score0.573
Threshold uncertainty score0.564

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0010.000
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 head0.011
GPT teacher head0.217
Teacher spread0.205 · 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

Citations20
Published2004
Admission routes2
Has abstractyes

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