Editing Research/Fiction Writing/AI Prose Strengthen (section)

== External research and documentation ==

=== Stanford HAI: detector bias and unreliability ===

Source: https://hai.stanford.edu/news/ai-detectors-biased-against-non-native-english-writers

Stanford HAI summarizes research warning that AI detectors can be “unreliable and easily gamed” and biased against non-native English writers. The article describes detectors being marketed to educators and journalists but highlights the core risk: algorithmic authorship judgments can wrongly flag human work.

Directive implication: do not use detector output as sole evidence. Preserve writing history, outlines, notes, version diffs, and citations when authorship might be questioned.

=== Liang et al. 2023: GPT detectors biased against non-native English writers ===

Source: https://arxiv.org/abs/2304.02819

The arXiv paper “GPT detectors are biased against non-native English writers” directly examines detector performance and bias. Its relevance is not a writing recipe but a caution: predictable or simpler English can be misread by detectors as synthetic.

Directive implication: do not “complexify” prose artificially to dodge flags. Instead, write to the audience and keep provenance records.

=== Pangram technical report ===

Source: https://arxiv.org/abs/2402.14873

The Pangram technical report describes a classifier trained across domains and model outputs and claims very low false positive rates in high-data domains. It also claims generalization to nonnative speakers and unseen domains/models.

Directive implication: detector technology varies widely. Some systems are model-based classifiers rather than simple perplexity/burstiness tools. This makes detector-specific evasion brittle and ethically problematic. The durable response is quality control plus transparent authorship.

=== GPTZero FAQ ===

Source: https://gptzero.me/faq/

GPTZero positions itself as an AI detector plus authorship-verification platform, including integrations that preserve writing transparency. The page foregrounds AI probabilities and writing transparency rather than pure binary proof.

Directive implication: when provenance matters, authorship history is stronger than retroactive style manipulation.

=== Slop Forensics Toolkit ===

Source: https://github.com/sam-paech/slop-forensics

Slop Forensics analyzes overrepresented lexical patterns in LLM output: repeated words, bigrams, trigrams, vocabulary complexity, and slop scores. Autonovel cites this as an inspiration for its anti-slop wordlists.

Directive implication: a useful revision pass can search for statistically overused LLM vocabulary and replace it with topic-specific language. But wordlists alone cannot prove authorship or guarantee quality.

=== EQ-Bench Slop Score ===

Source: https://eqbench.com/slop-score.html

EQ-Bench states that Slop Score is not a general AI detector. It measures overused AI-text patterns, especially slop words, “not X but Y” contrast patterns, and slop trigrams. It says the metric is optimized for creative writing and essays.

Directive implication: use slop scoring as a quality smell test. Do not optimize blindly for a score; a clean score can still be dull, false, or unethical.

=== MLA guidance on citing generative AI ===

Source: https://style.mla.org/citing-generative-ai/

The MLA page and comments emphasize disclosure/citation practices for generative AI, including acknowledging AI assistance and reviewing, editing, and supporting content with citations.

Directive implication: when AI materially contributes to prose, disclose according to the relevant venue’s rules. If output includes research claims, verify and cite primary sources.

=== Additional guidance located but access-limited in this environment ===

* OpenAI’s AI-text-classifier announcement page was Cloudflare-blocked here. It is still a commonly cited source in the broader detector debate because OpenAI later marked its classifier as unavailable due to low accuracy, but this specific session could not fetch the page content.
* Turnitin’s AI-detection product page was HTTP 403-blocked here. Treat Turnitin’s documentation as a venue-specific source to check directly where institutional rules depend on it.
* A Vanderbilt Brightspace article about Turnitin AI detection being unavailable was attempted but returned 404 for the URL tested. Do not rely on that URL without fresh verification.