References
- Bender, E. M., & Friedman, B. (2018). Data Statements for Natural Language Processing: Toward Mitigating System Bias and Enabling Better Science. Transactions of the Association for Computational Linguistics, 6, 587–604. 10.1162/tacl_a_00041
- Bowker, G. C. (1999). Sorting things out: classification and its consequences. MIT Press (Inside technology). 10.7551/mitpress/6352.001.0001
- Buurma, R. S., & Heffernan, L. (2018). Search and replace: Josephine Miles and the origins of distant reading. Modernism/modernity Print Plus, 3(1).
- Cer, D., et al. (2017). SemEval-2017 Task 1: Semantic Textual Similarity Multilingual and Crosslingual Focused Evaluation. In S. Bethard, et al. (Eds.), Proceedings of the 11th International Workshop on Semantic Evaluation (SemEval-2017) (pp. 1–14).
Association for Computational Linguistics . 10.18653/v1/S17-2001 - Cleverdon, C. W. (1967). The Cranfield tests on index language devices. Aslib Proceedings, 19(6), 173–194. 10.1108/eb050097
- Drucker, J. (2011). Humanities Approaches to Graphical Display. Digital Humanities Quarterly, 5(1). 10.63744/r4ysrh7ae534
- Edmond, J., & Lehmann, J. (2021). Digital humanities, knowledge complexity, and the five ‘aporias’ of digital research. Digital Scholarship in the Humanities, 36(Supplement_2), ii95–ii108. 10.1093/llc/fqab031
- Flanders, J., & Jannidis, F. (2018).
Data modeling in a digital humanities context: An introduction . In J. Flanders & F. Jannidis (Eds.), The shape of data in the digital humanities: Modeling texts and text-based resources (pp. 3–25). Routledge. 10.4324/9781315552941-1 - Liang, P., et al. (2022). Holistic evaluation of language models. arXiv. 10.48550/arXiv.2211.09110
- McCarty, W. (2005). Humanities computing. Houndmills, Basingstoke: Palgrave Macmillan.
- Miles, J. (1964). Eras & modes in English poetry (2nd rev. and enl. ed.). University of California Press.
- Rodríguez, P., & Boyd-Graber, J. (2021). Evaluation Paradigms in Question Answering. In M.-F. Moens, et al. (Eds.), Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing. EMNLP 2021 (pp. 9630–9642).
Association for Computational Linguistics . 10.18653/v1/2021.emnlp-main.758 - Wang, A., et al. (2019). GLUE: A Multi-Task Benchmark and Analysis Platform for Natural Language Understanding. arXiv. 10.48550/arXiv.1804.07461
- Wei, J., et al. (2022). Emergent Abilities of Large Language Models. arXiv. 10.48550/arXiv.2206.07682
DOI: https://doi.org/10.5334/johd.588 | Journal eISSN: 2059-481X
Language: English
Page range: 108 - 108
Submitted on: May 8, 2026
Accepted on: Jul 20, 2026
Published on: Aug 11, 2026
Published by: Ubiquity Press
In partnership with: Paradigm Publishing Services
© 2026 Jenny C. Y. Kwok, published by Ubiquity Press
This work is licensed under the Creative Commons Attribution 4.0 License.