Replication: Did the colonial mita cause a population collapse? What current surnames reveal in Peru
Bibliographic record
Abstract
This is the replication package for our paper titled "Did the colonial <i>mita</i> cause a population collapse? What current surnames reveal in Peru". We present quantitative evidence that the <i>mita </i>introduced by the Spanish crown in 1573 caused the decimation of the native-born male population. The mass baptisms after the conquest of Peru in 1532 resulted in the assignation of surnames for the first time. We argue that past mortality displacement and mass out-migration were responsible for differences in the surnames observed in <i>mita </i>and non-<i>mita </i>districts today. Using a regression discontinuity and data from the Peruvian Electoral Roll of 2011, we find that <i>mita </i>districts have 47 log points fewer surnames than non-<i>mita </i>districts, 65 log points fewer surnames that are present in only one district, and 93 log points fewer surnames that are solely present in one area (<i>mita</i> or non-<i>mita</i>).
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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 | 0.004 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.005 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.000 | 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 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".