v0.33.0 — discourse axes ship; citation placement refuted

The roadmap's last engineering item ships and its strongest measurement is
refuted. Both outcomes came from the same discipline: measure the thing against
a population it was not built on, and believe the result.

Two discourse axes, at two different units

tools/deai_discourse.py adds L2.cohesion (given/new linkage — how much of a
sentence's content vocabulary the sentence before it already used) and
L2.hedging (epistemic markers per 1,000 words). Both fire below the field's
tenth percentile, the opposite direction from every existing axis, because here
the defect is absence rather than excess. Roadmap rank 6, Deferred since
v0.26.1.

They measure at different units, and that is forced rather than chosen. Hedging
has no paragraph-scale lower tail at all: on the 27,917-paragraph wgl bank
its tenth percentile is exactly 0.000 in every one of the seven section buckets,
because a 40-word paragraph that hedges nowhere is entirely ordinary. A gate
there is one no passage can fall below, and the axis would have reported a
confident zero findings forever. Regrouped so one section is one unit, six of
seven buckets separate (p10 from 1.055 in data to 3.350 in discussion);
abstract stays flat and abstains, since an abstract is one passage. So
cohesion calibrates and detects per paragraph, hedging per section, and each
artifact records its own unit so the two can never be read against each other.

Hedging ships restricted to intro, and two independent measurements put the
restriction in the same place. Held-out transfer at the p10 gate on 203 refereed
papers: 7.89% in intro against 15.48%–26.77% everywhere else. Worst-of-six
generation regimes: 0.613 in intro against a 0.460 human-vs-human null, while
conclusion runs below chance at 0.376. Cohesion needs no restriction — it
transfers at 10.87% overall against a 10% design point, in every bucket, and
separates all six regimes in intro at worst-of-six 0.676 (null 0.515).

Both floors were measured, not picked. The cohesion sentence floor is 3 rather
than 4 because at 4 the 20-document ai tier offers 15 measurable introduction
paragraphs in total and at 3 it offers 62, with the separation unchanged (0.676
against 0.674). The hedging word floor is 150 because it is the first floor at
which every non-abstract bucket resolves, and 250 buys nothing while costing the
abstract bucket 86% of its sections.

Evidence: EVALUATION §19.

Citation placement: refuted on its own pre-registered condition

v0.32.0 recorded citation placement as the strongest model-free discriminator in
the whole record — section-matched rank AUC 0.866 in method, surviving the
section, length and human-vs-human controls — and declined to ship it for one
stated reason: all 173 machine documents came from a single generation process,
so one bank could not separate "AI cites more" from "these prompts made it cite
more". The condition for shipping was a second, independently produced bank.

Two banks of 20 documents each, same 20 topics, a different model (Codex
gpt-5.6-terra), differing in one line of prompt. With no citation instruction
the same statistic scores 0.053 — not the absence of separation, but
separation of nearly equal strength in the opposite direction. With one it
scores 0.734. Density swings 12.5× (1.00 to 12.55 citations per 1,000 words)
around a human median of 6.20, so the two machine extremes bracket the human
distribution instead of sitting on one side of it.

The signal is real and it is not about authorship: it is about which model,
prompted how. Refuted, and the bar it sets for anything after it — hold your sign
across independently produced banks — is stronger than the three controls
v0.32.0 applied. EVALUATION §20.

One (feature, unit) reference for every per-bucket axis

tools/deai_reference.py (roadmap rank 2, Deferred (elegance debt)) now owns
the quantile grid, the plateau-top percentile reader, the 30-unit sample floor,
the paragraph and section sweeps, the artifact loader — which had five copies
across the suite — and the calibration loop. It holds no policy.

It stopped being elegance debt the moment a second per-bucket axis existed, and
earned its keep immediately: its one invariant is that calibration and detection
share a unit and a grid, and that check is what caught the hedging axis
calibrating where no lower tail exists. cli_common.axis_main absorbs the
--calibrate-or-read-one-file CLI that three tools carried near-byte-equivalent
copies of, and deai_feedback.reference_block the reference= payload six
detectors spelled out separately.

Also

• deai_discourse is wired into ai_ism_lint behind --discourse (default on),
  and reports one axis status per feature, never a joint one — a field can
  support cohesion and not hedging, and does.
• Both README demos are now pinned to the axis set that produced them
  (--no-discourse); demo 1 arm C would otherwise draw a second advisory it was
  never scored with.
• tests/test_published_figures.py pins the two new references, including each
  bucket's p10 gate, and was negative-tested 4/4.
• tests/_profilefixture.py gives the axis tests one throwaway-profile builder;
  the two copies had already drifted on whether a record carries a source,
  which decides whether a section-unit reference can be built at all.
• 34 tools, 360 tests (19 files). Dead argparse/json/deai_metrics imports
  removed from four tools after the CLI extraction.
• The latency table is re-taken whole, not row by row: the interpreter floor
  alone moved 56 → 84 ms since v0.32.0, so no row from that table was comparable.
  The validator row was the worst of them, published at 359 ms against a measured
  2.3 s. A complete model-free pass is now 409 ms with the two new axes on
  (~325 ms above the floor). The first re-run was discarded — it read a 295 ms
  interpreter floor because it was competing with the session that started it.