Official sources. Your work, organized.
An AI detector produced a score, and the score produced a hearing. A detector score does not document how a piece of writing developed, and what weight it receives depends on the institution and its procedures. Gathered Work does two narrow things: it indexes each institution's published academic-integrity sources — quoted, dated, and linked — and it helps you organize your own drafts and records into a packet anyone can follow.
The measured performance of the accuser
Detection vendors advertise document-level false-positive rates near 1%. Independent testing returns higher figures, concentrated on particular writers. Liang et al. (Patterns, 2023) tested seven commercial detectors on 91 human-written TOEFL essays from a Chinese writing forum: an average of 61.3% were flagged as machine-generated, against substantially lower rates on the study's US eighth-grade comparison set. A 2024 national survey of teens and parents by Common Sense Media found 20% of Black teens reported a teacher had wrongly flagged their schoolwork as AI-generated, against 7% of white teens — a K–12 finding, cited here at its own scope. A 2026 preprint tested five keystroke-timing classifiers and reported forgery evasion above 99.8% under its specified attacks, alongside a proof that copy-typing machine text cannot be identified from timing data alone; it did not evaluate every process-tracking design.
Share of human-written text wrongly classified as machine-generated in the cited study. Sources in the footer.
These studies measure particular detectors on particular datasets; they do not yield one aggregate error rate. What they show is that false positives are real, that measured rates vary widely with the writer and the dataset, and that the writers most affected in the research are often those least positioned to contest a flag.
What each method can support
No method proves authorship, in either direction. That limitation is symmetric. What weight any of these methods receives depends on the institution and its procedures — which is why the place to start is the institution's own published sources. What a writer can document is process: that particular drafts existed in sequence, on dates.
| Method | Asserted claim | Supportable claim |
|---|---|---|
| Detector score | "This text is likely machine-generated" | A statistical estimate with documented, uneven error rates |
| Keystroke tracking | "A person typed this, here" | Timing data, forgeable, carrying no identity information |
| Version history | "This document developed over time" | Process evidence, subject to platform custody and retention |
| Cryptographic timestamp | "This exact draft existed at this time" | Independently verifiable by any party, with no vendor to trust, for as long as file and proof are kept |
A single timestamp establishes little, and a sequence of them documents exactly one thing: that particular files existed over time. It does not establish who created them, how they were created, or whether their development was authentic — the vault page states these limits plainly. What it gives the honest writer is narrower and real: a dated record that a score can be weighed against, verifiable by the party judging it.
The index, then the records
The institutions. The primary work of this site: one page per institution, quoting its published academic-integrity sources — the policy, the resolution procedure, the appeal path — with retrieval dates, direct links, and a change log. These are potentially relevant official sources, not a determination of which governs any case; confirm applicability with the institution. Live now: University at Buffalo and Liberty University; WGU, Adelphi, and UC Davis in preparation — the full list.
The records. For writing already questioned: a free checklist for saving what was received, preserving drafts and version history without modifying originals, and arranging the records chronologically. A paid packet organizes the writer's own files into that exhibit format; it selects nothing and pre-fills nothing based on anyone's institution or circumstances. If an outcome could affect enrollment, immigration status, a degree, professional licensing, or accommodations, consider obtaining qualified assistance before responding.
A free local hashing utility lives on the vault page for writers who keep fingerprint logs. It shows that particular files existed at particular times; it does not verify authorship or authentic development, and it is a supporting tool here, not the product.
This site is independent: no detector vendor and no law firm is behind it, and it sells neither detection nor legal services.
Who keeps this kind of record
Students facing integrity proceedings where the score is the case. Graduate students with flagged thesis chapters. Writers whose language background, dialect, or neurotype places them on the wrong side of the measured error distributions. And the professionals who advise them — education attorneys, ombuds, writing-center staff — for whom an organized, verifiable record shortens every case.
The underlying instrument is a century old. Laboratory notebooks established priority and integrity with dated, witnessed, sequential entries long before anyone thought to ask a machine its opinion of a finished page. This is that instrument, applied to writing.