Understanding Ethnolinguistic Differences: The Roles of Geography and Trade
Bibliographic record
Abstract
Abstract I study the role of trade on inter-ethnic linguistic differences in the long run. I hypothesise that the geographic environment of neighbouring ethnic groups determines their potential gains from trade, and that the frequency of inter-ethnic trade—and resulting social interactions—shape the co-evolution of language. As a test of this hypothesis, I build a georeferenced dataset to examine the border region of spatially adjacent ethnic groups, together with variation in the set of potentially cultivatable crops at the onset of the Columbian Exchange, to identify how variation in land productivity impacts linguistic differences between adjacent ethnic groups. I find that ethnic groups separated across geographic regions with high variation in land productivity are more similar in language than groups separated across more homogeneous regions. I develop a model to theoretically ground this link between land productivity variation and inter-ethnic trade, and provide empirical evidence in support of this mechanism, including direct evidence of a causal link between land productivity variation and an ethnic group’s reliance on trade for food and subsistence in pre-modern times.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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.
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 teacher head, 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".