Index Catalog // Deep Blue Data

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Filtering by: Creator Teplitskiy, Misha Keyword double-anonymization

Double-anonymous Peer Review Mitigates Country Homophily and the Harms of Low Reviewer Diversity: Deidentified data and replication code

Creator:

Dumlao, James M. Zumel and Teplitskiy, Misha

Description:

The data sources and methods used to process the raw data are described in the paper forthcoming in Science and the associated Supplementary Information. A preprint for an earlier version of this paper is available here: https://osf.io/preprints/socarxiv/754e3. These data are anonymized (see Methodology for details). Consequently, running the same code on these data vs. the data in the paper does not yield *identical* results but qualitatively similar ones.

Keyword:

peer review, double-anonymization, geographic diversity, homophily, fairness, and bias

Citation to related publication:

J. M. Z. Dumlao, M. Teplitskiy, Science, forthcoming. and Zumel Dumlao, J. M. and M. Teplitskiy. 2023. “The Effect of Reviewer Geographical Diversity on Evaluations Is Reduced by Anonymizing Submissions”. Retrieved (osf.io/preprints/socarxiv/754e3).

Discipline:

Science and Social Sciences