The short answer
Readers get a usable summary of why a study matters and what it does not establish, with links to the original paper and artifacts when available.
The decision standard is simple: preserve the source, state the limits, and make the next human check obvious. A useful article should reduce uncertainty without pretending that every unknown has been resolved.
What to examine
A responsible reading list identifies the question, method, dataset, result, limitation, funding, and whether the work is peer reviewed, a preprint, or a company report.
Start with scope. Identify the product, account, audience, jurisdiction, data, and decision involved. Then separate what was directly observed from what a vendor, researcher, regulator, or commentator says. Record dates because AI products, access rules, and prices change quickly.
A paper title and abstract are not enough for a strong claim. Avoid causal language when the study is observational or the evaluation does not support it.
A practical way to do it
- Read the method, results, and limitations sections.
- Check dataset scope, baselines, uncertainty, and artifact access.
- Write the caveat beside the finding, not several paragraphs later.
Keep the worksheet or test record with the draft. If another editor cannot reproduce the check from the saved evidence, the article is not ready.
Editorial guardrail
Do not fill a missing fact with a plausible sentence. Mark it as unknown, find a stronger source, narrow the claim, or remove it. Commentary belongs in a clearly labeled paragraph after the reported facts, not inside them.
Primary-source reading list
These are starting points, not automatic support for every sentence. The publishing editor must open each cited page and confirm the claim it supports on the day of review.
Bottom line
Readers get a usable summary of why a study matters and what it does not establish, with links to the original paper and artifacts when available.