Machines & Meaning · Capstone brief
HUM-03 · What tokenization erases
Show a meaning-bearing distinction that breaks under BPE/WordPiece — and why it matters for a text you care about.
The question
Show a meaning-bearing distinction that breaks under BPE/WordPiece — and why it matters for a text you care about.
Texts / materials
One literary, historical, or multilingual passage the student cares about. Tokenizer inspection (school-approved library or demo). Before/after token table. Short argument tying the break to interpretation — without claiming the model “misunderstands” as a mind.
Expected failure modes
Random weird tokens with no interpretive stake. Declaring tokenization evil in general. Inventing linguistics papers. Skipping the actual text.
Done looks like
Passage citation; token table; interpretive memo; non-claim list (this does not prove machines lack meaning; it shows a representation limit for this distinction).
Five C's
CT: representation vs meaning-talk. CR: text-tied example. CO: peer finds another break in the same passage. CM: memo. CZ: readers of the text whose distinction vanishes.
Mentor role
Language/literature teacher reviews interpretive stake. School-supervised.
Rubric calibration
R1: one passage, one distinction. R2: tokens shown. R3: human reading baseline. R4: scope limited to this case. R5: memo clear. R6: non-claim list on mind/meaning.
Two ways this goes wrong
(a) Token dump, no stakes. (b) “Therefore LLMs don’t understand language.”
Credit lane fit
Lane A immediately. No verified credit claim.