Bibliographic record
Abstract
Funding from the Canada Research Chair in Risk Management and the CIRPÉE is also acknowledged. We are indebted to Shirley Chenny for outstanding research assistance. We thank the staff at the Mauritius Archives for their help in locating the notarial acts, and Benoit Aboumrad for his help in collating information. We have benefited from very helpful comments from and discussions with Moez Bennouri, C. Robert Clark, Angel Hernando-Veciana, Simon Van Norden and Dezsö Szalay. Abstract: Evidence on adverse selection in slave markets remains inconclusive. We study this question through notarial acts on public slave auctions in Mauritius between 1825 and 1835, involving 4,286 slaves. In addition to slave characteristics, the acts document the identities of buyers and sellers. We use this information to determine whether the buyer of a slave was related (e.g. a relative or a spouse) to the original slave owner, and thus most likely better–informed than other bidders. Auction–theoretic models predict that bidding should be more aggressive when informed bidders are present in open–bids, ascending auctions, such as slave auctions. By proxying informed bidders by related bidders, our results consistently indicate that this is the case, pointing toward presence of residual adverse selection
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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 | 0.000 | 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 | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".