Using proprietary language models in academic research requires explicit justification
Open accessThe claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
For many scientists and public interest practitioners, lack of transparency is a reason to avoid closed models in their work.
“For many scientists and public interest practitioners, this lack of transparency is at best concerning, and often a reason to avoid such models in their work altogether [38, 46].”
From Laboratory-Scale AI: Open-Weight Models are Competitive with ChatGPT Even in Low-Resource Settings (2024) · cites this paper as [46]
The opening section identifies auditability and reproducibility concerns with closed models, and Additional information says scientists ideally would not use them. The paper does not address public-interest practitioners or establish how many people in either group avoid closed models for that reason. (LLM, conf=0.85)
Experimental — accuracy not yet measured
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
The citation lists haven’t been fetched yet
References 3 · Cited by 51
Research works printed but not linked: 1
- Spirling, A. Nature 616, 413 (2023).
- Authors
- Palmer, A., Smith, N. A., & Spirling, A.
- Affiliations
- New York University (US)
- University of Washington (US)
- Princeton University (US)
- Corresponding author
- Spirling, Arthur
- Venue
- Nature Computational Science
- Published
- 2023-12-21
- Volume
- 4
- Issue
- 1
- Pages
- 2-3
- ISSN
- 2662-8457 (ISSN-L)
- OpenAlex
- W4390052109
- Cited by
- 51
- References
- 3
- Citations by year
- 2026: 17 · 2025: 23 · 2024: 8 · 2023: 2 · 2022: 1
- FWCI
- 12.54
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Licence
- Locations
- Nature Computational Science (publishedVersion)
- PubMed (publishedVersion)
- Funders
- Princeton University
- University of Washington
- Allen Institute for Artificial Intelligence
- Office of the Dean for Research, Princeton University
- Topics
- Social Sciences › Safety Research › Ethics and Social Impacts of AI
- Social Sciences › Decision Sciences › Information Systems and Management › Academic Publishing and Open Access
- Social Sciences › Decision Sciences › Information Systems and Management › Scientific Computing and Data Management
- Keywords
- reproducibility, auditability, research ethics, open science
- MeSH terms
- Language
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 ›
Are you an author, or do you see a problem with this page? Request a review
