# Register collapse on borrowed phrasing 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: register-collapse-on-reuse - Category: Semantic (semantic) - Subcategory: reuse-failure - Also known as: valence inversion, source reused with its tone flipped - Status: Active. Currently signals low-effort writing. - Severity: high - Evidence grade: community-observed (named by practitioners, no formal measurement exists) - Languages: en - Added: 2026-08-15 - Updated: 2026-08-15 - Page: https://feedsquad.com/ai-tells/register-collapse-on-reuse ## Description Source material is carried across with its emotional register inverted. A line written about hardship arrives as a recommendation. The words survive the move and their meaning does not. ## Why it reads as machine-written Reuse at scale copies strings and drops the context that told a reader how to take them. The canonical case is a travel guide from August 2023 that listed a food bank among the things to see in a city and repeated a line from the charity's own site about arriving hungry. The charity had written it about need. A human editor reading for sense catches this in one pass, which is why it is such a clean signal about the process: nobody read it. Microsoft disputed that a language model produced the guide and attributed it to human error, so this entry describes an unreviewed pipeline rather than a model, and that denial ships with it. ## Detection Type: judge (a rubric for a lightweight model judge) Rubric for a lightweight model judge: ```text Task: decide whether any passage carries a subject associated with harm, loss or need in a register meant for leisure, opportunity or entertainment. Step 1. List the subjects the text describes: places, services, events, organisations. Step 2. For each, name the register the subject ordinarily belongs to. Grief, emergency, poverty, illness, criminal proceedings, and industrial accident all carry one. Restaurants, museums and shops carry another. Step 3. Name the register the sentence uses. Recommendation, invitation, ranking and enthusiasm mark the leisure register. Step 4. Mark every mismatch, and quote the specific words that carry it. Escape hatches: memorial and disaster tourism written with acknowledgement, which is an established genre; satire and black comedy where the tone is the point; sources whose own register is celebratory, such as a festival marking a historical event; charities and services describing their own work in the language they choose for it; reclaimed language used by the affected community. Decision. FLAG on one unacknowledged mismatch involving a subject in the harm register. Do not flag on discomfort alone; the test is whether the text has silently changed what the subject is for. Output: the subject, the two registers, the quoted words, and FLAG or PASS. ``` ## Examples Constructed specimens. Written for this index. Never quoted from anyone's posts. ### Specimen 1 Before, exhibiting the tell: > Our third stop is the memorial garden, a peaceful green space the city opened after the 2016 fire. Bring a picnic and make an afternoon of it. After, repaired: > The memorial garden was opened after the 2016 fire and the names are set into the north wall. It is open to the public every day. The city asks visitors to keep it quiet around the anniversary in June, when families come. Note: The first version borrows a description written for one purpose and files it under another. The repair keeps the fact and restores what the place is for. ## False positives Who legitimately writes this way. Rushed human aggregation makes this error constantly, and always has. Travel desks recycle press material, listings are compiled from databases by people with no time to read them, and translation flattens the markers that told a reader how a sentence was meant. Memorial and disaster tourism is a real genre with its own conventions, and writing about a site of suffering as a place worth visiting is legitimate when the piece acknowledges what it is. The vendor in the documented case denied that any language model was involved, which is a useful reminder that this tell identifies an unreviewed process rather than a tool. The only reliable question is whether anyone read the sentence in its new home. ## Model attribution Disputed at the source. The vendor said the guide came from algorithmic techniques with human review rather than from a language model. This entry treats the tell as a signal about review, not about the generator. ## Sources 1. CBC, Microsoft travel guide recommends Ottawa food bank https://www.cbc.ca/news/canada/ottawa/artificial-intelligence-microsoft-travel-ottawa-food-bank-1.6940356 (tier: press; accessed 2026-08-14) ## Status history Ids are permanent. A retired tell keeps its id and its page. - 2026-08-15, Active: One incident, one press source, and a vendor denial that a language model was involved. That denial is why the grade is community-observed rather than corroborated, and the entry keeps the tell while declining to claim its cause. ## 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.