AI-generated stories favour stability over change: homogeneity and cultural stereotyping in narratives generated by gpt-4o-mini

Hermann Wigers; Jill Walker Rettberg

REVIEW 4 major objections 7 minor 1 cited by

AI-generated stories favour stability over change: homogeneity and cultural stereotyping in narratives generated by gpt-4o-mini

T0 review · 4 major / 7 minor · reviewed 2026-08-06 · deepseek-v4-flash

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Pith's one-line read Across 236 countries, gpt-4o-mini tells the same story: a protagonist returns to a small town, restores tradition, and organises a community event.

desk verdict A useful, honest dataset and a genuinely interesting qualitative reading, but the paper's headline prevalence claim—'overwhelmingly conform'—is not actually measured, so the verdict stands or falls on whether the authors can either code a sample or soften the claim. read the letter →

arxiv 2507.22445 v1 pith:RUI4XQ4F submitted 2025-07-30 cs.CL cs.AI

Jill Walker Rettberg , Hermann Wigers This is my paper · ORCID

classification cs.CLcs.AI

keywords largelanguagemodelsgenerativeAInarratologybiasculturalnarrativestandardizationsyntheticimaginarygpt-4o-mini

verification ladder T0 review T1 audit T2 compute T3 formal

The pith

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The reading

This paper tries to establish that a large language model asked to write a story for any nationality does not so much express that culture as flatten it: across 11,800 stories generated for 236 countries with the prompt “Write a 1500 word potential {demonym} story”, gpt-4o-mini repeatedly produces one plot. A protagonist lives in or returns to a small town, faces a minor threat to community or tradition, and resolves it by organising a festival or community event, ending in nostalgia and reconciliation. Real-world conflict is sanitised, romance is almost absent, and national colour is reduced to surface symbols such as fjords, olive trees, or trains. The authors argue that this plot-level uniformity is a distinct form of AI bias—narrative standardisation—that should be studied alongside representational bias, because AI writing tools are increasingly embedded in daily work and culture.

What carries the argument

The machinery is the dataset probe itself: a single minimal prompt repeated 11,850 times (50 stories for each of 236 countries plus 50 stories with no demonym), together with the concept of a synthetic imaginary—the model's statistically learned space of word associations that it draws on to imitate a “{demonym} story”. The minimal prompt is designed to push the model toward its default output, and the analysis then extracts the shared plot formula through word-frequency counts, word trees, sentiment summaries, and close readings of selected countries. The formula—return to a small town, minor conflict, community event, restored tradition—is the load-bearing object that carries the homogenisation claim.

What would settle it

Annotate a random sample of, say, 200 of the 11,800 stories for plot elements (protagonist returns home, small-town setting, community event as resolution, presence of romance, presence of direct conflict) and compare the distribution across countries with the distribution of the same elements in human-authored stories matched by country. If within-country variance in the generated stories is as large as between-country variance, the single-formula claim fails; if human-authored stories show the same concentration of return-and-community-event plots, the “human stories are more diverse” contrast fails.

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Extended reading notes

Core claim

On the paper's own terms, the discovery is that gpt-4o-mini's synthetic imaginary has a default story grammar. Whatever the nationality requested, the model produces a protagonist who returns home to a small town or village, faces a conflict that is downplayed or externalised, and resolves it by reconnecting with tradition and organising the community. Even stories for countries in active conflict follow this pattern: Palestinian stories emphasise standing firm and community organising rather than direct confrontation, Israeli stories individualise conflict behind vague opponents, and both lean on olive trees as symbols. The American stories are the clearest instance, with 23 of 50 titles beginning “The Last Train” and a structure close to the Hallmark-movie formula. The paper proposes calling this narrative standardisation: a formal bias at the level of plot, distinct from the word-and-image representational bias that dominates AI-bias research.

Load-bearing premise

The load-bearing premise is that one minimal English prompt and one model snapshot reveal the model's default story structure, rather than being an artifact of that exact wording, language, or model version.

Editorial extensions

If this is right

If the default story grammar is as stable as claimed, AI-assisted writing tools that suggest or revise prose will tend to push storytellers toward nostalgia, reconciliation, and community-organising endings, crowding out plots built on conflict, change, or romance.
AI-generated stories already posted to online story sites are becoming training data for future models, so the pattern can feed on itself unless deliberately countered.
Measuring AI bias only at the level of words and images misses a systematic bias at the level of plot; cultural-alignment benchmarks would need to include narrative structure, not just values or stereotypes.
For narratology, the finding supports the idea of “surface narration”: generated stories look story-like sentence by sentence but lack the coherence and causal drive of human-authored narratives.

Reading between the lines

Editorial extensions of the paper, not claims the author makes directly.

If the same experiment is repeated with prompts that specify genre, era, or emotional register, the uniformity may persist or break; the paper's own prompt-dependence caveat suggests this is the next test.
A quantitative human baseline—annotating human-authored stories from the same countries with the same plot categories—would settle whether the claimed homogenisation is a property of the model or of the prompt.
The narrow model choice (gpt-4o-mini, English prompts, early-2025 snapshot) means the finding is a lower bound on narrative standardisation; other models and multilingual prompts could show more or less flattening.
The paper's closing anecdote about an AI-assisted journal edit hints that the same normalising pressure may extend to academic prose, not only fiction, though that connection is left undeveloped.

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Signed reviews

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Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, and a circularity audit.

Desk Editor's Note

read the letter

Two things you should know. First, the paper ships something real: a public dataset of 11,800 gpt-4o-mini stories for 236 countries, with code, and a transparent methods section that admits exactly what was done manually and what was computed. Second, the central claim in the abstract—stories 'overwhelmingly conform to a single narrative plot structure'—is not backed by a measured prevalence. The authors read four countries closely, sampled others, and state that saturation was reached; that is a description of their reading experience, not a systematic annotation. The word-frequency and sentiment analyses track surface vocabulary (heart, village, spirit, train), not plot structure. So the load-bearing quantitative claim is actually a qualitative observation.

That said, the qualitative observation is good. Their account of the 'small town, return, restore tradition, organize a festival' arc is illustrated with enough examples to be credible as a pattern. The Palestinian/Israeli comparison is careful and nuanced: the word trees for 'stand' and 'fight' show real differences, and their reading of how conflict is sanitized is persuasive within the subset they read. The train-symbolism section is also stronger than typical AI-bias writing. The Hallmark-movie connection is a reasonable hypothesis and they cite the NYT analysis of 424 movies.

The soft spots are real but mostly addressable. No human baseline: 'human-authored stories are far more diverse' is asserted, and we have no way to compare. The 50-word summaries used for sentiment are generated by the same model, so the model's own defaults are baked into the ancillary analysis. And it is one model and one prompt; the authors explicitly ask whether their result is a prompt artifact. They do not oversell in the discussion, which makes the abstract's 'overwhelmingly' especially jarring.

The dataset and code are properly archived, the paper is readable, and the authors are honest about limits. I would send it to referees, with the clear demand that the prevalence claim be either operationalized (a coded random sample with inter-rater agreement, say) or rewritten as a clearly qualitative finding. If prevalence is high under systematic coding, the paper becomes an important data point for narrative bias. If it is not, the paper is still a useful close reading plus a reusable dataset. Either way it deserves referee time, and not just a desk rejection.

Referee Report

4 major / 7 minor

Summary. The paper analyzes a corpus of 11,850 stories generated by gpt-4o-mini (50 stories for each of 236 countries, plus 50 with no country specified) from the single prompt "Write a 1500 word potential {demonym} story." The authors report that, despite surface-level national symbols, the stories overwhelmingly conform to a single plot structure: a protagonist in a small town or village, often returning from a city, resolves a minor conflict by reconnecting with tradition and organizing a community event. They argue that real-world conflicts are sanitized, romance is nearly absent, and nostalgia and reconciliation replace narrative tension, constituting a distinct form of AI bias termed "narrative standardisation." The evidence combines word-frequency analyses, word trees, sentiment analysis on 50-word summaries, and close reading of stories from four countries plus sampling from others.

Significance. If the central empirical claim holds, this is a valuable and thought-provoking contribution. The dataset and analysis code are openly available, and the paper connects narratology, cultural AI bias, and computational literary studies in a way that few studies do. The word-tree comparisons for Palestinian and Israeli stories, the train symbolism analysis in American stories, and the plot diagram for Norwegian stories are illuminating and provide concrete, falsifiable observations. The proposed distinction between representational bias and structural or narrative bias is conceptually useful. However, the headline claim that the stories 'overwhelmingly conform to a single narrative plot structure across countries' is currently supported primarily by qualitative close reading rather than by a systematic, reproducible measure of plot structure. The paper also relies on a single prompt, a single model, and no human-story baseline, which limits the generality of the conclusion.

major comments (4)

[Methodology ("Generating the dataset") and "Norwegian stories" (Figure 6)] The central claim that stories 'overwhelmingly conform to a single narrative plot structure across countries' (Abstract) is not supported by a direct quantitative measurement of plot structure. The authors state that 'The qualitative analysis was primarily done by Jill Walker Rettberg, based on a close reading of the Norwegian, American, Palestinian and Israeli stories and sampling stories from many other countries,' and Figure 6 is explicitly described as 'not generated computationally but by reading the stories and manually annotating them to identify shared plot points.' The word-frequency and sentiment analyses (Figures 1-2) measure surface vocabulary and affect, not plot events or their sequence. The paper needs a reproducible coding scheme for plot elements, applied to a representative sample (or, ideally, the full dataset), with inter-rater reliability and reported prevalence rates for the proposed standard plot. Without this, the claim of cross-country 'overwhelming' uniformity is an overgeneralization from a small hand-read subset.
[Conclusion ("This requires further research") and "Generating the dataset"] The authors themselves raise the question, 'Is the similarity caused by our prompt, or is it a clue to how LLMs are inferring narrative structures from the training data...?' This is a load-bearing issue. With one prompt, one model (gpt-4o-mini), one generation window, and no comparison across prompt variants or models, the observed plot pattern could be an artifact of the particular instruction 'Write a 1500 word potential {demonym} story,' the length constraint, or the specific model version. The paper should include at least a small prompt-variation study (e.g., different phrasings, no length specification) and/or a second model to establish that the stability-and-tradition plot is a default narrative tendency rather than a response to this specific wording.
[Introduction and Conclusion] The contrast with human-authored stories is asserted but not demonstrated: the paper states that 'Human-authored stories are far more diverse' and presents the Hallmark-movie comparison and the Norwegian Askeladden example as illustrations. Since the framing of the paper is about homogenisation relative to human narrative diversity, the authors should provide a matched baseline of human-authored stories from the same countries coded with the same plot scheme, or explicitly rescope the claim to state that the finding concerns homogeneity within the AI-generated corpus. Without a human baseline, the broader cultural-loss claim remains an interpretive leap rather than an empirical result.
["Generating the dataset" and "Word frequency"] The computational analyses do not actually cover all 236 countries on an equal footing. The authors note that 'Most, but not all, of the French, German and Indonesian stories are in French, German and Indonesian' and 'Many of the Danish stories are in Danish,' yet the word-frequency and noun-phrase analyses use an English-language spaCy pipeline and English lemmatization. Consequently, the reported counts for these countries substantially underrepresent the vocabulary of the generated stories. This limitation should be stated explicitly in the figure captions and in the limitations paragraph, and it further qualifies the cross-national comparisons in Figures 1 and 2.

minor comments (7)

[Introduction] The word 'abandonned' should be 'abandoned' in the sentence about the train station.
["Norwegian stories"] The word 'reminiscient' should be 'reminiscent.'
["Palestinian and Israeli stories"] The word 'sanatised' should be 'sanitised.'
["American stories"] The phrase 'Mason-Dixie country' appears to be a typo for 'Mason-Dixon' (or the Mason-Dixon line).
[Footnote 5] The aside about Trump tariffs is informal and likely to date the paper; consider removing it or moving it to a general note about the country list.
[Conclusion] The anecdote about Paperpal Preflight is interesting but digresses from the narrative-standardisation argument; consider moving it to a separate section on AI in scientific publishing or shortening it substantially.
["Generating the dataset"] The sentiment analysis is performed on 50-word summaries generated by gpt-4o-mini rather than on the full stories, and the sentiment model was trained on Twitter data with no 'neutral' class. This is a limitation of the exploratory sentiment findings and should be mentioned wherever such findings are interpreted.

Circularity Check

0 steps flagged · score 0.0 of 10

No circularity: the paper's central claim is an empirical observation of LLM outputs, supported by human close reading and independent computational tools, with no fitted parameter, self-referential derivation, or equation-level reduction.

full rationale

The paper's load-bearing claim is that gpt-4o-mini generates stories that overwhelmingly conform to a single plot structure. This claim is an empirical description of generated outputs, not a derived quantity. The plot structure was identified by human close reading: the authors state that 'The qualitative analysis was primarily done by Jill Walker Rettberg, based on a close reading of the Norwegian, American, Palestinian and Israeli stories and sampling stories from many other countries', and that Figure 6's plot diagram 'was not generated computationally but by reading the stories and manually annotating them to identify shared plot points'. No parameter is fitted to a subset of the data and then presented as a prediction; no equation is defined in terms of the conclusion; no uniqueness theorem or prior result by the authors is invoked to force the interpretation. The ancillary use of gpt-4o-mini to produce 50-word plot summaries is explicitly limited to sentiment analysis with Distilbert, and the authors acknowledge its limitations; it does not generate the structural claim. The paper's own caveat that 'Is the similarity caused by our prompt, or is it a clue to how LLMs are inferring narrative structures from the training data...?' shows that the prompt-dependence concern is honestly flagged rather than hidden. Self-citations to the authors' own dataset and software are transparent data-availability statements, not load-bearing argumentation. The weakness that the 'overwhelmingly' prevalence claim rests on qualitative reading rather than a systematic coded sample is a methodological evidence concern, not circularity. Under the given criteria, there is no circular step to quote, and the correct finding is no significant circularity.

Assumptions & free parameters 0 free parameters · 5 assumptions · 0 invented entities

No free parameters were fitted to data, and no new entities were postulated. The central claim depends on assumptions about the representativeness of one model, one prompt, and the countries chosen for close reading, plus the assumption that surface word-frequency patterns can support plot-structure claims. These assumptions are stated in the methodology but not independently verified.

assumptions (5)

domain assumption gpt-4o-mini is representative of 'AI-generated stories' generally.
Title and abstract generalize from one model to AI-generated stories; the paper does not test other models or versions, so this assumption is load-bearing for the broad framing but not for the dataset-specific observations.
ad hoc to paper The prompt 'Write a 1500 word potential {demonym} story' elicits the model's default story structure rather than a prompt artifact.
Authors report that removing 'potential' changes the output language and that early tests shaped the prompt; the conclusion explicitly asks 'Is the similarity caused by our prompt?', so the assumption is acknowledged but untested across prompts.
domain assumption Close reading of Norwegian, American, Palestinian and Israeli stories plus sampling from other countries is representative of all 236 countries.
Qualitative analysis section says the authors read these subsets and 'We have not read all 11,850 stories', yet the central plot claim is stated for the whole dataset.
domain assumption Word frequency and sentiment analyses can indicate plot-structure uniformity.
The reported quantitative measures capture surface vocabulary and emotion labels, not plot events or narrative sequences; the plot-structure conclusion is established by qualitative reading rather than by the quantitative measures.
domain assumption Human-authored stories are more diverse than the generated corpus.
Asserted in the Introduction without a quantitative baseline or cited comparative dataset; this is a background assumption for the 'homogenisation' claim.

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Cite this review

Pith. "Pith review of AI-generated stories favour stability over change: homogeneity and cultural stereotyping in narratives generated by gpt-4o-mini." pith.science (2026). https://pith.science/paper/RUI4XQ4F

@misc{pith2026250722445,
  author       = {Pith},
  title        = {Pith review of: AI-generated stories favour stability over change: homogeneity and cultural stereotyping in narratives generated by gpt-4o-mini},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/RUI4XQ4F}},
  note         = {Machine review of arXiv:2507.22445}
}

read the original abstract

Can a language model trained largely on Anglo-American texts generate stories that are culturally relevant to other nationalities? To find out, we generated 11,800 stories - 50 for each of 236 countries - by sending the prompt "Write a 1500 word potential {demonym} story" to OpenAI's model gpt-4o-mini. Although the stories do include surface-level national symbols and themes, they overwhelmingly conform to a single narrative plot structure across countries: a protagonist lives in or returns home to a small town and resolves a minor conflict by reconnecting with tradition and organising community events. Real-world conflicts are sanitised, romance is almost absent, and narrative tension is downplayed in favour of nostalgia and reconciliation. The result is a narrative homogenisation: an AI-generated synthetic imaginary that prioritises stability above change and tradition above growth. We argue that the structural homogeneity of AI-generated narratives constitutes a distinct form of AI bias, a narrative standardisation that should be acknowledged alongside the more familiar representational bias. These findings are relevant to literary studies, narratology, critical AI studies, NLP research, and efforts to improve the cultural alignment of generative AI.

Figures

Figures reproduced from arXiv: 2507.22445 by the authors.

**Figure 1.** shows a wordcloud of the most common words across all the stories. Although hearts and feelings are common across all the stories,6 there are also significant differences from country to country. American stories, as told by gpt-4o-mini, prioritise feel, with town as number two, heart and story still important and, surprisingly, train finishing up the list of the five most frequent words. Polish stories feature fo… view at source ↗

**Figure 2.** This box plot shows the twenty most frequent words across all 50 stories for each country, not taking language into account. The country using each word the most is marked using its two-letter country code. PF is French Polynesia, MO is Macao, US is United States, VU is Vanuatu, MW is Malawi, RW is Rwanda, EH is Western Sahara, JP is Japan, MC is Monaco, TD is Chad, GR is Greece, SY is Syria, CU is Cuba, PS is Pales… view at source ↗

**Figure 3.** Wordtree showing words that come after “stand” in Palestinian stories. Page 10 of 17 Open Research Europe 2025, 5:202 Last updated: 29 JUL 2025 [PITH_FULL_IMAGE:figures/full_fig_p010_3.png] view at source ↗

Figures from the paper (3 more)

**Figure 4.** Figure 4: Wordtrees showing how “stand” is used in Norwegian stories. Page 11 of 17 Open Research Europe 2025, 5:202 Last updated: 29 JUL 2025 [PITH_FULL_IMAGE:figures/full_fig_p011_4.png]

**Figure 5.** Figure 5: Word tree showing how “fight” is used in Palestinian stories. Page 12 of 17 Open Research Europe 2025, 5:202 Last updated: 29 JUL 2025 [PITH_FULL_IMAGE:figures/full_fig_p012_5.png]

**Figure 6.** Figure 6: Most of the Norwegian stories follow this structure. This plot diagram was not generated computationally but by reading the stories and manually annotating them to identify shared plot points. Page 14 of 17 Open Research Europe 2025, 5:202 Last updated: 29 JUL 2025 [P…

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Cited by 1 Pith paper

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