How EditorScore Works
EditorScore combines deterministic text metrics with AI-assisted review. The goal is to provide useful editorial signals quickly while being honest about uncertainty and limitations.
Writing Score
The scoring tool combines readability and text statistics with model-generated feedback for grammar, clarity, and engagement. Readability is based on standard formulas such as Flesch-style measures, while the AI layer adds qualitative suggestions.
The overall score is a weighted blend rather than a single scientific truth. Different writing goals may justify different tradeoffs.
Mode-Aware Feedback
Writing modes adjust the lens used for qualitative feedback. For example, academic mode emphasizes structure and evidence framing, while resume mode focuses more on action verbs, specificity, and professional tone. The underlying text metrics remain comparable, but the AI suggestions are guided by the chosen context.
AI Detector
The AI detector is heuristic. It looks for stylistic patterns and sentence-level signals that may correlate with AI-generated text, but it cannot prove authorship. False positives and false negatives are possible, especially for edited, translated, formulaic, or highly polished writing.
OCR Cleanup
OCR output is produced from uploaded files and then cleaned into plain text, headings, and warnings. This improves readability, but scanned documents can still contain extraction errors, layout confusion, broken tables, or missing characters. OCR output should always be reviewed manually before it is cited or submitted.
Journal Generator
The journal generator is designed to create structured article blueprints rather than pretend to write verified scholarship from thin air. It uses retrieved reference candidates as a starting point and inserts placeholders where evidence or study details still need to be supplied by the user.
Peer Review Tool
The peer review output is an editorial-style draft meant to help identify likely revision areas. It is not a formal journal decision, not an expert certification, and not a replacement for domain review.
Limitations
- Model outputs can be incomplete, overconfident, or wrong.
- OCR quality depends heavily on the source document.
- Detector results are probabilistic, not definitive.
- Generated citations and references must be verified before formal use.
- Scores are useful for revision, not as a universal measure of writing value.