Cited by 4Memory, Trauma, and CommemorationMemory, violence, and historyCultural Identity and Heritage

ChatGPT’s interpretation of contested memoryscapes favours the voice of current governments, capitalism and the far-right

Open access
González-Cantera, Tania, Bonacchi, Chiara·2025·International Journal of Heritage Studies

Abstract

This study examines political biases in ChatGPT-4o’s interpretations of memoryscapes linked to dictatorial regimes in Spanish-speaking countries. Using a least-to-most prompting model and a Discourse-Historical Approach (DHA), we analysed responses in both Spanish and English about three key memorial sites: the Valley of the Fallen (Spain), the ESMA Museum and Site of Memory (Argentina), and the Monument to Salvador Allende (Chile). Findings show a preference for dominant, capitalist, and government-aligned perspectives, often reinforced by far-right rhetoric, particularly in Spanish language. The research highlights generative AI’s influence in shaping heritage narratives and the challenges of ensuring unbiased and multi-vocal outputs. This study advances the fields of cultural heritage, digital humanities, and education, while also shedding light on the wider implications of ChatGPT’s outputs for memory politics and democratic governance.

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.

A few free checks a day · papers already verified open instantly · results are public

Integrity signals

Registry

Crossref · 10.1080/13527258.2025.2543736

Cites

7 of 7 references verified0 citation errors · 0 not checkable

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.

A few free checks a day · papers already verified open instantly · results are public

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?

  • Algorithmic systems often privilege dominant cultural narratives, altering the meanings consumers encounter and the products they see.

    “Other work finds that algorithmic systems often privilege dominant cultural narratives, altering the meanings consumers encounter and the products they see (González-Cantera & Bonacchi, 2025).”

    From Marketing to the Machine: Rethinking Global Marketing for the AI Era (2026) · cites this paper as González-Cantera & Bonacchi, 2025

    The indexed text (section “1 Active”; no page given) reports that ChatGPT-4o’s interpretations of three contested memorial sites favoured dominant, capitalist, and government-aligned perspectives, supporting the point about privileged narratives. It does not address consumers or the products they see, and the finding is specific to ChatGPT’s heritage interpretations rather than algorithmic systems generally. (LLM, conf=0.85)

    Experimental — accuracy not yet measured
  • Research on model-based curation shows that models apply their own interpretive frames, often prioritizing dominant narratives or reducing complex cultural categories to simplified tropes.

    “Research on model-based curation shows how models apply their own interpretive frames, often prioritizing dominant narratives or reducing complex cultural categories to simplified tropes (González-Cantera & Bonacchi, 2025).”

    From Marketing to the Machine: Rethinking Global Marketing for the AI Era (2026) · cites this paper as González-Cantera & Bonacchi, 2025

    The paper’s title identifies a study of ChatGPT interpreting contested memoryscapes and favouring the voices of current governments, capitalism and the far right. That supports the point about dominant narratives, but the passages available from our copy do not establish the claim about simplifying complex cultural categories into tropes or about model-based curation generally (searched: simplified tropes, cultural categories, dominant narratives). (LLM, conf=0.78)

    Experimental — accuracy not yet measured
  • At the representational level, generative systems actively construct category logics and evaluative frames.

    “At the representational level, generative systems actively construct category logics and evaluative frames, amplifying concerns about synthetic content, hallucination, and narrative distortion raised in emerging AI research (Campbell et al., 2022;González-Cantera & Bonacchi, 2025;Spennemann, 2023).”

    From Marketing to the Machine: Rethinking Global Marketing for the AI Era (2026) · cites this paper as González-Cantera & Bonacchi, 2025

    The passage labeled “1 Active” (page unavailable) says ChatGPT-4o’s interpretations of three memorial sites favour dominant, capitalist, and government-aligned perspectives and influence heritage narratives. That supports the claim about evaluative framing, but the available passages do not establish that it constructs category logics or address synthetic content and hallucination. (LLM, conf=0.78)

    Experimental — accuracy not yet measured
  • Training data carry the cultural assumptions, interpretive frames, and semantic patterns of their sources, which can privilege certain meanings and downplay others.

    “Training data carry the cultural assumptions, interpretive frames, and semantic patterns of their sources, which can privilege certain meanings and downplay others (González-Cantera & Bonacchi, 2025).”

    From Marketing to the Machine: Rethinking Global Marketing for the AI Era (2026) · cites this paper as González-Cantera & Bonacchi, 2025

    The retrieved passages contain bibliographic material and an abstract, but no full-text passage addressing whether training data carry cultural assumptions, interpretive frames, or semantic patterns. Searches for those terms and for training-dataset bias did not locate a passage supporting the attributed claim. (LLM, conf=0.70)

    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.

Cites

Flagged first, then by citations

Cited by

Sorted by citations

Authors
González-Cantera, T., & Bonacchi, C.
Affiliations
  • Universitat de Girona (ES)
  • University of Edinburgh (GB)
Corresponding author
González-Cantera, Tania
Venue
International Journal of Heritage Studies
Published
2025-08-29
Volume
31
Issue
11
Pages
1508-1530
ISSN
  • 1352-7258 (ISSN-L)
  • 1470-3610
OpenAlex
W4413817875
Cited by
4
References
7
Citations by year
2026: 3 · 2025: 1
FWCI
4.43
Type
journal article
Language
en
Open access
Licence
cc-by
Locations
  • International Journal of Heritage Studies (publishedVersion)
  • Edinburgh Research Explorer (University of Edinburgh) (submittedVersion, cc-by)
Funders
  • la Caixa” Foundation (LCF/BQ/DR21/11880004)
  • The Leverhulme Trust (PLP-2022-297)
Topics
  • Social Sciences › Psychology › Social Psychology › Memory, Trauma, and Commemoration
  • Social Sciences › Psychology › Social Psychology › Memory, violence, and history
  • Social Sciences › Arts and Humanities › Archeology › Cultural Identity and Heritage
Keywords
GPT-4o, memory politics, memorial sites, Valley of the Fallen, far-right rhetoric, capitalism, Spanish-speaking countries, least-to-most prompting, discourse-historical approach

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