Bibliography – Reporting and Documentation Methods

Selected DH research and resources bearing on, or utilized by, the WE1S project.
(all) Distant Reading | Cultural Analytics | Sociocultural Approaches | Topic Modeling in DH


Gebru, Timnit, Jamie Morgenstern, Briana Vecchione, Jennifer Wortman Vaughan, Hanna Wallach, Hal Daumeé III, and Kate Crawford. “Datasheets for Datasets.” ArXiv:1803.09010 [Cs], 2019. http://arxiv.org/abs/1803.09010. Cite
Mitchell, Margaret, Simone Wu, Andrew Zaldivar, Parker Barnes, Lucy Vasserman, Ben Hutchinson, Elena Spitzer, Inioluwa Deborah Raji, and Timnit Gebru. “Model Cards for Model Reporting.” Proceedings of the Conference on Fairness, Accountability, and Transparency - FAT* ’19, 2019, 220–29. https://doi.org/10.1145/3287560.3287596. Cite
Holland, Sarah, Ahmed Hosny, Sarah Newman, Joshua Joseph, and Kasia Chmielinski. “The Dataset Nutrition Label: A Framework To Drive Higher Data Quality Standards.” ArXiv:1805.03677 [Cs], 2018. http://arxiv.org/abs/1805.03677. Cite
Bender, Emily M., and Batya Friedman. “Data Statements for Natural Language Processing: Toward Mitigating System Bias and Enabling Better Science.” Transactions of the Association for Computational Linguistics 6 (2018): 587–604. https://doi.org/10.1162/tacl_a_00041. Cite
Bounegru, Liliana, Jonathan Gray, Tommaso Venturini, and Michele Mauri. A Field Guide to “Fake News” and Other Information Disorders. Amsterdam: Public Data Lab, 2017. http://fakenews.publicdatalab.org/. Cite
Collins, Gary S., Johannes B. Reitsma, Douglas G. Altman, and Karel G.M. Moons. “Transparent Reporting of a Multivariable Prediction Model for Individual Prognosis Or Diagnosis (TRIPOD): The TRIPOD Statement.” Annals of Internal Medicine 162, no. 1 (2015): 55. https://doi.org/10.7326/M14-0697. Cite