# Nominalization overload Part of [The AI Tells Index](https://feedsquad.com/ai-tells). A tell signals low effort. It does not identify an author. Skilled writers produce every shape listed here, some of them daily, and automated detectors misread those writers for it at rates measured above 60 percent on non-native English prose. Nothing in this index proves that a machine wrote anything. Read an entry as one piece of evidence to weigh against the false-positive notes printed beside it. ## Facts - Id: nominalization-overload - Category: Structural and syntactic (structural) - Subcategory: nominalization - Also known as: the implementation of, utilization, noun-heavy construction - Status: Active. Currently signals low-effort writing. - Severity: medium - Evidence grade: peer-reviewed (a published study measures the pattern) - Languages: en - Added: 2026-08-15 - Updated: 2026-08-17 - Page: https://feedsquad.com/ai-tells/nominalization-overload ## Description Verbs freeze into abstract nouns, and then a weak verb has to carry them. Reinhart and colleagues report the construction in GPT-4o output at roughly twice the human rate, with a Cohen's d of 1.23. The number is a table value in the paper body, and the abstract does not carry it. This entry rests on that single measurement, which is the only published count of the pattern we found. ## Why it reads as machine-written The nominal version reads as institutional because that is where it comes from. It also loses the actor: once the doing becomes a thing, nobody has to be named as having done it. Writing that is trying to be exact tends to move the other way, from the noun back to the verb, and gets shorter as it goes. ## Detection Type: statistical (a measured metric against a threshold with a stated basis) Metric: nominalizations-per-1000-words Threshold basis: > No threshold ships. The 2.1 times figure and the Cohen's d of 1.23 are body-level table values comparing model corpora against human corpora, and no per-document human distribution has been published. A number set here would be the fabricated precision this index catalogues. Measure it, compare it against the writer's own earlier drafts, and treat a rise as a FeedSquad review trigger. ## Examples Constructed specimens. Written for this index. Never quoted from anyone's posts. ### Specimen 1 Before, exhibiting the tell: > The implementation of the new review process resulted in an improvement in the identification of defects prior to release. The introduction of clearer expectations led to an increase in participation across the wider organisation. The reduction of duplication and the standardisation of terminology contributed to a general improvement in the quality of the documentation produced. The continuation of the initiative is dependent upon the allocation of resource in the next planning cycle, and a recommendation regarding the extension of the process to adjacent teams is under preparation. After, repaired: > We changed the review rota on 4 May. Two people now read every pull request before it merges, and the number of bugs caught before release doubled over the following month. Note: Eleven frozen verbs across three sentences, and nobody in any of them is doing anything. The repair names the actor and the date, and the verbs do the work the nouns were doing. Figures in this repair are invented for the specimen. ## False positives Who legitimately writes this way. Legal and administrative drafting is built from nominalizations, and work by Martinez and colleagues found that laypeople asked to write law produce the same construction with no training at all, which makes it a property of the genre rather than of the writer. Grant applications, standards documents and clinical protocols reward the nominal form because it names a defined thing that other clauses point back to. Check whether the actor is also missing; a named actor sitting beside nominal vocabulary is usually a lawyer, not a thin draft. ## Model attribution Measured in GPT-4o at roughly twice the human rate and in Llama 3 70B Instruct at between 1.5 and 2 times. No vendor documents suppressing it. ## Sources 1. Reinhart et al., Do LLMs write like humans? PNAS 122(8) (arXiv:2410.16107) https://arxiv.org/abs/2410.16107 (tier: peer-reviewed; accessed 2026-08-14) ## Status history Ids are permanent. A retired tell keeps its id and its page. - 2026-08-15, Active: The only published measurement of the pattern is Reinhart, which found it at roughly twice the human rate in 2024-era models. Nothing since reports the gap closing, and no vendor documentation names the construction as suppressed. ## License CC BY 4.0. https://creativecommons.org/licenses/by/4.0/ Attribution: The AI Tells Index, feedsquad.com/ai-tells Reuse the data, including commercially. Keep the attribution line. --- Dataset version 1.0.0. Schema version 1. Part of [FeedSquad](https://feedsquad.com). Built by [Herman Foundry](https://hermanfoundry.com) from Levi, Finnish Lapland.