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Cited by 5Library Science and Information LiteracyEducational Strategies and EpistemologiesMisinformation and Its Impacts

The DOI matches its Crossref record; 30 of the 32 references it cites were found in a registry or an index.

Mnemonic evaluative frameworks in scholarly publications: A cited reference analysis across disciplines and AI-mediated contexts

Open access
Tomaszewski, Robert·2025·The Journal of Academic Librarianship
DOI 10.1016/j.acalib.2025.103113
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Abstract

Mnemonic evaluative frameworks have become central to information literacy instruction for assessing information credibility. Widely used tools such as CRAAP, CARS, ACT UP, and SIFT remain underrepresented in scholarly literature and insufficiently aligned with emerging information challenges. This study uses cited reference analysis in the Scopus database to examine 16 mnemonic evaluative frameworks across 280 peer-reviewed journal articles, conference papers, and review articles. Citation patterns were analyzed by year, discipline, institutional affiliation, and source title. Findings reveal that while legacy models like CRAAP and CARS retain the most citations, newer frameworks such as SIFT and RADAR are proportionally more cited in AI-related literature. A subset of 49 AI-focused citing documents indicates a disciplinary shift from Library and Information Sciences toward Computer Science, Engineering, Business, and Decision Sciences since 2022. These results highlight the need for adaptive, systems-aware models that address credibility challenges associated with generative AI and algorithmic curation. In response, this study introduces the CAT Test ( Check, Ask, Think ), a three-part evaluative framework designed to help learners assess AI-generated content by corroborating claims, interrogating model reasoning, and reflecting on platform influence. The findings inform instructional design and contribute to ongoing conversations about algorithmic transparency and credibil…

Overview

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Registry

Crossref · 10.1016/j.acalib.2025.103113

Cites

30 of 30 references verified0 citation errors · 0 not checkable

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Claims other papers make about this one

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Citations

Cites

Show all 32 references

Research works printed but not linked: 10

  1. Bartz, 2002, Teaching skepticism via the CRITIC acronym and the skeptical inquirer, Skeptical Inquirer, 26, 42
  2. Blakeslee, 2004, The CRAAP test, LOEX Quarterly, 31, 6
  3. Caulfield, 2019, SIFT (the four moves) [Blog post], Hapgood
  4. Elsevier
  5. Hervieux, 2020, The ROBOT test [Blog post], The LibrAIry
  6. McManus, 2013, Don’t be fooled: Use the SMELL Test to separate fact from fiction online [Blog post], MediaShift
  7. Schrock, 1998, The ABCs of web site evaluation, Classroom Connect, 5, 4
  8. Smith, 1981, Citation analysis, Library Trends, 30, 83
  9. Tomaszewski, 2024, Enhancing critical thinking with the CAT Test: Check, Ask, Think, Faculty Focus
  10. Tomaszewski, 2025, Mnemonic evaluative frameworks in scholarly publications: A citation analysis [Poster presentation]. 2025 CSU Libraries Conference, California State University, Fullerton, CSU ScholarWorks

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Metadata

Authors
Tomaszewski, R.
Affiliations
California State University, Fullerton (US)
Corresponding author
Tomaszewski, Robert
ORCID
Tomaszewski, Robert
Venue
The Journal of Academic Librarianship
Publisher
Elsevier BV
Published
2025-09
Volume
51
Issue
5
Pages
103113
ISSN
0099-1333 (ISSN-L)
DOI
10.1016/j.acalib.2025.103113
OpenAlex
W4412763721
Cited by
3
References
32
Citations by year
2026: 3
FWCI
9.03
Type
journal article
Language
en
Open access
Licence
Locations
The Journal of Academic Librarianship (publishedVersion, cc-by)
Funders
California State University, Fullerton
Topics
  • Social Sciences › Library and Information Sciences › Library Science and Information Literacy
  • Social Sciences › Psychology › Developmental and Educational Psychology › Educational Strategies and Epistemologies
  • Social Sciences › Sociology and Political Science › Misinformation and Its Impacts
Keywords
information credibility, information literacy instruction, SIFT, radar, generative AI, AI-generated content evaluation, algorithmic curation, algorithmic transparency, citation patterns, disciplinary differences, academic librarianship

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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.

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CoChat. (2026). Citation check: Mnemonic evaluative frameworks in scholarly publications: A cited reference analysis across disciplines and AI-mediated contexts. https://app.cochat.ai/papers/10.1016/j.acalib.2025.103113
The paper
Tomaszewski, R. (2025). Mnemonic evaluative frameworks in scholarly publications: A cited reference analysis across disciplines and AI-mediated contexts. The Journal of Academic Librarianship. https://doi.org/10.1016/j.acalib.2025.103113