L'effet de la “Distance Linguistique” sur le Commerce Bilatéral: Basé sur le Canada et la Chine
Bibliographic record
Abstract
Abstract This paper develops a measure of “linguistic distance” between languages, which is used as a dummy variable in Gravity Model. By this model, the paper discusses the trade relationships of Canada and China with those countries which speak English as the first language and as the second language. The results show that the proportion of the population that use English as second language will be more important factor than those use English as second language for the bilateral trade with Canada and China. Considering the specification error in this model, it can also reach the same results. Key words: Linguistic distance; Bilateral trade; Gravity model Resume Cet article developpe une facon d’indiquer la “distance linguistique” en tant que variable fictive dans le modele de gravite. A travers les analyses sur le modele, nous discutons la relation commerciale de la Chine et du Canada entre les pays ou l’anglais est la langue maternelle et ceux ou l’anglais est la langue seconde. Selon les resultats, du point de vue de la regression du commerce bilateral, comme un facteur principal, la proportion de l’utilisation de l’anglais comme la langue seconde est plus importante que les facteurs dans les pays ou l’anglais est la langue maternelle. Si l’on rend compte des erreurs de specification, les resultats restent les memes. Mots-cles: Distance linguistique; Le commerce bilateral; Modele de gravite
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.011 | 0.001 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".