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Record W2939058056 · doi:10.1093/aepp/ppz026

The Value of Terroir: A Historical Analysis of the Bordeaux and Champagne Geographical Indications

2019· article· en· W2939058056 on OpenAlexaff
Catherine Haeck, Giulia Meloni, Johan Swinnen

Bibliographic record

VenueApplied Economic Perspectives and Policy · 2019
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicWine Industry and Tourism
Canadian institutionsUniversité du Québec à Montréal
FundersKU Leuven
KeywordsTerroirWineGeographyViticultureValue (mathematics)ExploitEconomic geographyRegional scienceEconomyEconomicsArtComputer science

Abstract

fetched live from OpenAlex

Abstract Previous studies on the value ofterroir, or more generally geographical indications (GI), used hedonic techniques. We use historical data and exploit temporal and geographical variations in the introduction of wine GIs in early twentieth century France to study the impact on the price of specific wines in the years and decades following their introduction. We find large effects of GIs on prices of some Champagne wines, but no significant impact on Bordeaux or other Champagne wines.

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 score0.065
Threshold uncertainty score0.128

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0030.003
Science and technology studies0.0000.001
Scholarly communication0.0020.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0030.000

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 head0.005
GPT teacher head0.213
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

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

Citations37
Published2019
Admission routes1
Has abstractyes

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