The DOI matches its Crossref record; 31 of the 31 references it cites were found in a registry or an index.
Can linguists distinguish between ChatGPT/AI and human writing?: A study of research ethics and academic publishing
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In the citing paper
“Previous studies suggested that human users often fail to recognise AI-generated text and are generally overconfident in their ability to do so (Casal & Kessler, 2023;Fleckenstein et al., 2024).”
From “ ChatGPT can make mistakes. Check important info.” Epistemic beliefs and metacognitive accuracy in students' integration of ChatGPT content into academic writing (2025) · cites this paper as Casal & Kessler, 2023
In the citing paper
“This is heightened by the fact that even experts, such as professional linguists and teachers, are unable to effectively distinguish between human-written and AI-generated text (Casal & Kessler, 2023;Fleckenstein et al., 2024).”
From “ ChatGPT can make mistakes. Check important info.” Epistemic beliefs and metacognitive accuracy in students' integration of ChatGPT content into academic writing (2025) · cites this paper as Casal & Kessler, 2023
In the citing paper
“Large language models (LLMs) have achieved a level of fluency and coherence that makes their outputs increasingly indistinguishable from those by humans.1,2”
In the citing paper
“By adopting similar designs with objective linguistic criteria, we found that both the reviewer success rate and answer alternation reasons (“Suspicious wording” and “Both not mentioning critical features”) were associated with the writing style (“Familiarity and voice” and “Specificity or vagueness of details”) rather than the sentence-level writing quality and coherence34.”
From Towards a holistic framework for multimodal LLM in 3D brain CT radiology report generation (2025) · cites this paper as [34]
In the citing paper
“traits of “Familiarity and voice” (29%), “Specificity or vagueness of details” (37.60%), “Continuity and Coherence” (33.33%), and “Writing quality at sentence level” (33.33%) were significant factors when medical experts attribute a human origin behind a radiology report”
From Towards a holistic framework for multimodal LLM in 3D brain CT radiology report generation (2025) · cites this paper as [34]
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On the Dangers of Stochastic Parrots: Can Language Models Be Too Big? 🦜
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Metadata
- Authors
- Casal, J. E., & Kessler, M.
- Affiliations
- University of Memphis (US)
- University of South Florida (US)
- Corresponding author
- Casal, J. Elliott
- Venue
- Research Methods in Applied Linguistics
- Publisher
- Elsevier BV
- Published
- 2023-12
- Volume
- 2
- Issue
- 3
- Pages
- 100068
- ISSN
- 2772-7661 (ISSN-L)
- OpenAlex
- W4385628481
- Semantic Scholar
- 678298191a35694083bd01d4fac2f9a229dab98b
- Cited by
- 261
- References
- 28
- Citations by year
- 2026: 69 · 2025: 97 · 2024: 83 · 2023: 12
- FWCI
- 65.31
- Type
- journal article
- Language
- en
- Open access
- closed
- Licence
- Locations
- Research Methods in Applied Linguistics (publishedVersion)
- Topics
- Social Sciences › Education › Academic integrity and plagiarism
- Physical Sciences › Computer Science › Artificial Intelligence › Authorship Attribution and Profiling
- Social Sciences › Library and Information Sciences › Academic Writing and Publishing
- Keywords
- ChatGPT, large language models, AI-generated text detection, peer review, journal editors, research ethics, scholarly publishing, artificial intelligence in research, applied linguistics journals
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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