ECIN Replication Package for "The Long-Run Agglomeration Effects of Early Agriculture in Europe"
Bibliographic record
Abstract
These codes and data allow replication of all results in the paper "The Long-Run Agglomeration Effects of Early Agriculture in Europe", to be published in Economic Inquiry. Abstract of the paper: We study the effects of an early introduction of agriculture (Neolithic Transition) on modern agglomeration, using a new dataset on carbon dated organic materials found at archaeological sites in Northern Europe. We find a positive effect of early agriculture, in particular within countries, which contrasts with a negative or zero correlation found in older data covering a larger region that includes both Europe and the Middle East. However, we argue that these patterns are actually consistent with each other, because early agriculture can exert (1) positive long-run effects on urban agglomeration, while also giving rise to (2) extractive state institutions, which can hamper economic development. While (2) shows up in comparisons between countries and regions with more varied long-run development paths, such as the Middle East and Europe, (1) is easier to find within more homogeneous regions, such as Northern Europe, and especially within countries. We corroborate this interpretation with evidence of earlier city development close to sites with earlier Neolithic Transitions.
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 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 | 0.005 | 0.041 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.004 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.461 | 0.156 |
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".