Machine Summaries and the Missing Footnote

Analytical software, including systems marketed as artificial intelligence, can retrieve text, cluster paragraphs, and draft a summary of a table. Those are reading aids. They are not a warrant that the draft is true, complete, or suitable as a household instruction. This note is about the missing footnote: the openable source. Maple Investment Hub does not use a model as an adviser, and it does not ask readers to automate a buy or sell decision from a prompt.

The educational temptation is to treat fluency as authority. A fluent paragraph about a filing can still invent a number, drop a qualifier, or mash two years together. Literacy after 2020 includes this new genre of error alongside the old ones.

Alternative data — satellite pictures, card receipts, scraped prices — is sometimes sold as a way around the footnote. For a beginner, it is still a source problem. Who collected it, over which window, with which gaps? A novel feed is not exempt from the checklist. Novelty is not warrant.

A draft, not a proof

A model that predicts the next word can produce a sentence that looks like a citation. Looking like a citation is not the same as pointing at a page you can open. Research, even beginner research, still needs a source that exists outside the chat window. If the source cannot be opened, the sentence is a hypothesis about a source.

This is not an argument against using tools. It is an argument against retiring the habit of the index note — units, windows, authors — just because the restatement arrived in a second. The faster the restatement, the more the checking has to be explicit, because there is less time for doubt to happen by accident.

What tools can support

They can help you find a document you then read. They can list section headings so you know where a risk discussion sits. They can translate a dense paragraph into simpler English that you then verify. Those uses stay on the educational side of the line. They become something else if the tool is asked “what should I do with my savings?” — a question this site will not answer and a question a model is not entitled to answer for you.

Provenance problems

Training data are a mixture of dates, qualities, and incentives. A model may not know when a page was updated. It may mix a textbook definition with a marketing page. It may omit the jurisdiction that makes a sentence true in one country and false in another. Canada has its own regulatory vocabulary; a fluent paragraph written for another legal system can sound universal while being local.

Provenance means being able to say where a number came from. If the answer is “the model,” you do not yet have provenance. You have a generator. Put the generator beside the table. If they disagree, the table wins until you have a reason to distrust the table — a reason that is also a document, not a vibe.

Hallucinated citations are a special case. A title that sounds like a working paper is not a working paper until you can open it. Treat unmatched citations as missing sources, not as extra reading you have not found yet.

Speed as opacity

Speed hides labour. A human summary takes time; during that time a reader can notice a missing axis. Instant text removes that pause. Educational use of AI should restore a pause on purpose: a checklist, a second source, a printed page.

• Does every figure appear in an openable source?
• Is the window stated?
• Is the jurisdiction stated?
• Has a qualifier (“estimate,” “unaudited,” “backtest”) been dropped?
• Has the draft turned a description into an instruction?

If the last box is ticked, stop. Instruction is outside this hub even when a human writes it. It is more outside when a model writes it, because there is no one to hold to a licence.

A checking drill

Take a public statistical table. Ask a tool to summarise it. Then, without looking at the summary, write three facts you can see with your eyes. Compare. Circle mismatches. Repeat with a methodology page, which models often flatten. The drill teaches distrust that is specific — distrust of uncited numbers — rather than a vague fear of computers or a vague faith in them.

Ask the same question twice, hours apart. If the numbers move without a new source, you have watched a generator, not a database. Stability is not truth, but instability without a citation is a warning light. Notice extra adjectives (“attractive,” “resilient”). Educational prose can live without them.

Conclusion

A machine summary can support financial research as a librarian and a drafter. It cannot stand in for a source, a licence, or a household decision. Speed is useful only if the pause is put back by hand. The last note in this volume gathers checking habits into a reading order for claims — including claims that arrive with a machine sheen.