AI-generated conjecture · below the evidence/publication boundary

The frequency-graded syllabus

Status: Anticipated · untested

Status is derived only from the shepherd-authored triage/prediction data above -- community submissions and claims are a separate overlay and can never change it (see the participation panel below).

This is a proposed connection between two domains, generated by a language model. It is not an article and not evidence: it sits below the evidence/publication boundary. A quantitative prediction and a named kill-dataset are attached (when registered) so the claim stays falsifiable rather than merely evocative.

Claim (verbatim)

Modern language courses teach the commonest words first, a principle usually credited to twentieth-century corpus linguistics. Old Babylonian scribal schools drilled students on long thematic lexical lists — trees, wooden objects, stones, professions — whose internal ordering is conventionally explained as associative or taxonomic. The conjecture: within each thematic section, entry order tracks the word's actual frequency in the working administrative and legal corpus, because teachers whose students could draft a real barley loan by year two out-competed teachers who began with rare curiosities. That would make the lexical tradition a frequency-ranked corpus statistic compiled nearly four millennia before frequency dictionaries, and it would explain why list recensions quietly reorder entries across centuries: they were re-ranking to follow drifting usage.

Prediction clause (verbatim)

Within sections of the Old Babylonian Nippur recension of Ura (ur5-ra = hubullu), the Spearman correlation between an entry's list position and its lemma frequency in contemporary administrative and legal documents will be positive with rho of at least 0.3 in a majority of sections. Primary clause, which decides the verdict: a sign test across all sections with 20 or more matchable entries shows significantly more positively-correlated sections than negatively-correlated ones. Secondary clause: correlations computed against a corpus 300 years older fit worse than against the contemporary corpus.

Kill-dataset (verbatim)

ORACC (DCCLT, the Digital Corpus of Cuneiform Lexical Texts) for list order, crossed with lemma frequencies from BDTNS and Archibab administrative and legal documents.

Nobody has run this test. The kill-data is named above. If you can run it — or you know the paper that already settles it — claim the kill or submit the prior. Kills and priors are credited here, by name, as they come in.

In the atlas

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Provenance

Run: Fresh agent generation · model: claude-fable-5

Composed blind by claude-fable-5 from internal knowledge only, with zero tool calls, and emitted directly as a single JSON text message.

Novelty / leakage triage

anticipated in the literature — this exact test has never been run

Searched lexical-list ordering vs. corpus frequency. Prior work correlates early lexical lists with economic-record usage and treats ordering as mnemonic/bureaucratic, but no study testing within-section Ura order against lemma frequency in the working corpus was located.

Predictions

No prediction registered yet.

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