MétaCan
Menu
Back to cohort

Exporting to new destinations and the effects of tariffs: the case of meat commodities

2009· article· en· W2051186636 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.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
aboutThe title or abstract carries a Canadian signal from the geographic lexicon.

Bibliographic record

VenueAgricultural Economics · 2009
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicGlobal trade and economics
Canadian institutionsUniversité LavalUniversity of Lethbridge
Fundersnot available
KeywordsTariffLiberalizationInternational economicsProbit modelFree tradeEconomicsEstimationInternational tradeDestinationsProbitBusinessAgricultural economicsEconometricsGeographyMarket economy

Abstract

fetched live from OpenAlex

Abstract This study uses a random parameter probit estimation to examine the effects of tariff liberalization on the probability of establishing new trading relationships in meat commodities. Our simulation results indicate that the effects of tariff reductions decrease with distance, but increase with the level of development. The probabilities of trade increase at an increasing rate with the size of tariff reductions thus justifying calls for ambitious liberalization schemes. Canada and Mexico are the NAFTA countries that are most likely to export in response to EU tariff reductions on bovine and poultry meats, while Brazil and Argentina emerge as the MERCOSUR countries most likely to penetrate the EU bovine meat market after EU tariff reductions. Uruguay's probability to export poultry meat is most responsive to EU tariff reductions.

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)0.000
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: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.378
Threshold uncertainty score0.346

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
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 head0.025
GPT teacher head0.190
Teacher spread0.165 · 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