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Record W229166190 · doi:10.48416/ijsaf.v16i1.280

Consolidation in the North American Organic Food Processing Sector, 1997 to 2007

2020· article· en· W229166190 on OpenAlexaboutno aff
Philip H. Howard

Bibliographic record

VenueAgEcon Search (University of Minnesota, USA) · 2020
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicOrganic Food and Agriculture
Canadian institutionsnot available
Fundersnot available
KeywordsDiversification (marketing strategy)Consolidation (business)BusinessHorizontal integrationMainstreamFood processingOrganic farmingIndustrial organizationCommerceMarketingGeographyAgriculturePolitical scienceFinance

Abstract

fetched live from OpenAlex

Significant structural changes have accompanied the phase-in of a national organic standard in the United States over the last decade. The organic processing sector was particularly amenable to change due to its location downstream from production, where concentrations of capital encounter fewer biological barriers, and currently benefit from greater economies of scale. Consolidation of this emerging industry in the US and neighboring Canada is characterized visually using information graphics. These graphics provide a broad overview of the current industry structure by depicting the processes of horizontal integration and concentric diversification. Horizontal integration has occurred through acquisitions and strategic alliances, although these transactions are often hidden from consumers through ‘stealth’ ownership. Concentric diversification has occurred through the introduction of organic versions of mainstream brands, and the introduction of private label organics. These trends are expected to continue, and strongly support the conventionalization thesis as it applies to off-farm segments of the organic food industry.

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)0.003
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 score0.648
Threshold uncertainty score0.701

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.004
Science and technology studies0.0020.001
Scholarly communication0.0030.002
Open science0.0010.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0060.001

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 head0.029
GPT teacher head0.201
Teacher spread0.172 · 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

Citations61
Published2020
Admission routes1
Has abstractyes

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