Terminology Silos and the Consilience Gap: A Quantitative Audit of Cross-Domain Vocabulary

Abstract

Interdisciplinary consilience is claimed everywhere and measured almost

nowhere. This paper measures one of its basic preconditions: whether

scientific domains share vocabulary. We generalize the keyword-taxonomy

audit of the QNFO research program (a seven-domain organization whose own

canonical taxonomy proved strictly partitional — 334 of 335 keywords occur

in exactly one domain) into a quantitative instrument and apply it to an

external sample of six arXiv disciplines (240 recent abstracts; 466

technical compound terms). The result: terminology silos are the norm, not

an organizational accident. 97.2% of the external technical vocabulary

occurs in exactly one discipline; the shared core (terms in three or more

disciplines) is empty; and the 2.8% of terms that do bridge disciplines are

massively enriched in method-level vocabulary (machine learning, language

models, upper bounds) rather than structural concepts (Fisher exact

p = 8.5e-7, odds ratio 70). On the QNFO corpus, semantically linked papers

within a program share title vocabulary (mean Jaccard 0.11 vs 0.03 random),

while the cross-domain semantic bridges the program itself cites carry zero

lexical signal unless an author deliberately wrote the bridge into a title.

We conclude that the consilience gap is in part a vocabulary gap: shared

terminology is rare, structurally biased toward methods, and built rather

than emergent. The paper closes with the infrastructure response — bridge

vocabularies as first-class research infrastructure, taxonomy engineering,

and semantic mapping — and states where its premises end.

1. Introduction

A researcher in quantum foundations, a researcher in algebraic number

theory, and a researcher in machine learning can work on the same structural

object — a nested, hierarchical partition of a state space — and never

discover each other. The first calls it a measurement hierarchy, the second

calls it a valuation, the third calls it a cluster tree. Their vocabularies

do not overlap; their results do.

This is the terminology silo problem. It is a commonplace complaint of

interdisciplinary research that "we all mean the same thing but use

different words." It is rarely quantified. This paper quantifies it, at two

scales: the internal scale of a single research organization (the QNFO

program, seven research domains sharing one canonical keyword taxonomy) and

the external scale of six scientific disciplines sampled from arXiv.

The motivation is not lexicographic. Large-scale scientific progress is

increasingly bottlenecked at the vocabulary layer: literature search,

citation graphs, knowledge graphs, and AI-assisted synthesis all operate

primarily on lexical matching, and lexical matching systematically misses

cross-domain correspondences. If the shared vocabulary of science is 3% of

its technical terms, then keyword-based discovery is a 3%-recall instrument

for the connections that interdisciplinary consilience depends on. That is

the gap this paper measures and then proposes infrastructure for.

The stake for a reader is direct: if you search for related work and miss

the field that already solved your problem under a different name, you are

paying the terminology silo tax. This paper measures the size of the tax

(97% of technical vocabulary is domain-local), its structure (what little

is shared is method, not substance), and what to do about it (build bridge

vocabularies; do not wait for them to emerge).

2. The seed audit: a seven-domain organization's own vocabulary

The QNFO research program spans seven domains — ultrametric physics (UMP),

the laws of form (SLB), infomatics (INM), paradigm engineering (CFE),

consilience research (RES), a cloud-native platform (PLT), and interactive

demos (DEM) — and maintains a canonical keyword taxonomy of 335 terms for

GitHub discovery (docs/QNFO-KEYWORD-TAXONOMY.md v1.0, 2026-08-05). The

program's central claim is that the seven domains are seven vocabularies for

one structural object: nested hierarchical partition logic.

A computational audit of that taxonomy (10.5281/zenodo.22071421) found the

claim lexically unsupported. 334 of 335 keywords occur in exactly one

program; exactly one keyword (complexity-measure) occurs in two; none

occurs in three or more. The taxonomy's own bridge subsections — named

bridge concepts inside each program — are program-local anchors: they name

connections without instantiating shared vocabulary. A Fisher exact test of

whether the four bridge families (valuation, hierarchy, distinction, bound)

coincide with load-bearing vocabulary (keywords shared by three or more

programs) gives p = 1.0: no enrichment whatsoever. The consilience, if it

exists, is semantic — carried by corpus-level links between records — not

lexical.

This single-case result invites the general question: is the QNFO taxonomy

an organizational artifact, or a microcosm of science? The rest of this

paper treats it as a hypothesis source and builds an instrument that can be

applied to any domain partition.

3. Definitions and instrument

Three quantities, all computed from a domain-keyword partition (a set of

domains, each with a set of technical terms):

1. Partitionality index — the fraction of distinct keywords occurring

in exactly one domain. A partitionality of 1.0 means total vocabulary

isolation; 0.0 means every term is shared.

1. Bridge share — the fraction of distinct keywords occurring in two or

more domains. The complement of local vocabulary.

1. Shared core — keywords occurring in three or more domains (a

load-bearing shared vocabulary in the sense of the seed audit).

Plus a structural probe: a one-sided Fisher exact test on the

bridge x general-family contingency, where the general family is a curated

list of structurally general method and pattern terms (machine learning,

neural networks, Monte Carlo, phase transitions, upper bounds, and similar

vocabulary that plausibly travels across fields). The test asks whether

bridge terms are drawn preferentially from the general family rather than

uniformly from all vocabulary.

Vocabulary extraction. For the external sample, technical vocabulary is

extracted from paper titles and abstracts as compound terms (bigrams of

content words), after removing a large stopword set (function words and

generic science shells such as "model", "system", "theory", "study",

"field", "structure"). Bigrams are used because modern technical vocabulary

is overwhelmingly compound (phase-transition, neural-network, monte-carlo,

p-adic); single tokens are too ambiguous to discriminate domains. Per

domain, the 80 most frequent bigrams (frequency at least 2 in the sample)

form the domain vocabulary. The extraction is deterministic: no randomness,

fixed seed policy for any sampling.

The instrument reproduces the published seed audit exactly (validation 9/9:

335 distinct keywords, 334 local, one shared-by-two, zero core,

53/282 family-bridge contingency, Fisher p = 1.0), so the external numbers

below rest on a validated pipeline.

4. Results I: six disciplines, one vocabulary wall

Six arXiv categories were sampled (40 most recent abstracts each, fetched

2026-08-24): quantum physics (quant-ph), number theory (math.NT), machine

learning (cs.LG), genomics (q-bio.GN), materials science

(cond-mat.mtrl-sci), and economic theory (econ.TH). Vocabulary extraction

yielded 466 distinct technical bigrams across the six domains.

The partitionality hypothesis (H-SILO-1: at least 90% of vocabulary

domain-local) is confirmed: 97.2%. The shared core is empty at the

three-domain threshold. Interdisciplinary rhetoric notwithstanding, six

disciplines share essentially no technical vocabulary beyond chance.

The 13 bridge terms are the interesting residue:

• Method vocabulary: machine-learning, language-model, language-models,

foundation-models, upper-bound (5 of 13).

• Shared objects: boron-nitride, quantum-defects, defects-zno,

double-substitutional, optically-quantum, candidate-quantum,

candidates-optically (7 of 13 — two disciplines working on the same

materials).

• Shared application: drug-discovery (1 of 13).

The structural probe quantifies the method bias: of the 13 bridges, 5 are

method/pattern terms; of the 453 domain-local terms, only 4 are. The Fisher

exact test on the bridge x general-family contingency gives

p = 8.5e-7 with odds ratio 70.2 — bridge vocabulary is seventy times more

likely to be method vocabulary than domain-local vocabulary is. What

travels between disciplines is not the structural substance (hierarchies,

valuations, measures) but the toolkit (learning, models, bounds).

The QNFO seed case shows the same structure in extremis: bridge share

0.0030, and its own bridge families show zero enrichment against

load-bearing vocabulary (p = 1.0). The seed's bridge vocabulary is

domain-anchored (ostrowski-theorem, idele-class-group) rather than

method-level — an organization whose bridge infrastructure is homegrown, not

imported.

5. Results II: the QNFO corpus — semantic links and lexical silence

The external sample measures vocabulary; the QNFO corpus (578 titled

records in the living paper) measures whether semantic relatedness is

lexically visible. Three probes.

Probe 1 — the knowledge-graph link network is program-local. The QNFO

knowledge graph's paper-to-paper semantic edges (CITES, DEPENDS_ON,

MOTIVATES, REFINES, BRIDGES, REFERENCES, SUPERSEDES, LINKSTO, RELATESTO)

resolve to 40 pairs of corpus records. Classifying each record into its

program by title-vocabulary overlap with the taxonomy, 8 pairs are

same-program and 0 pairs are cross-program (32 pairs involve at least one

unclassifiable endpoint). The graph links papers within programs; it has

not yet built the cross-program network. The link infrastructure is itself

siloed.

Probe 2 — within-program links are lexically visible. Same-program

linked pairs share title vocabulary at mean Jaccard 0.115, against 0.028

for random pairs (seeded, same cardinality): a +0.086 elevation. Within a

program, shared vocabulary tracks semantic relatedness. This is the

partitional null: vocabulary signals structure only inside a domain.

Probe 3 — the cited cross-domain bridges carry zero lexical signal.

The umbrella program paper (10.5281/zenodo.22073477, section 5.1) cites

three corpus records as the semantic bridges that carry the consilience:

measurement stratigraphy linking epistemology to valuation theory

(10.5281/zenodo.21705220), the valuation-without-reals framework

(10.5281/zenodo.21803677), and ultrametric topology in semantic memory

(10.5281/zenodo.19564091). The first pair (measurement-stratigraphy vs

adelic-shannon-theory) has title-vocabulary Jaccard 0.0000; the third

pair (silent-radix cryptography vs ultrametric numeration) also 0.0000.

The consilience is lexically invisible where no author built the bridge.

The single visible exception proves the rule: the valuation-without-reals

record shares the token "valuation" with measurement stratigraphy at

Jaccard 0.333 — because its author deliberately wrote the bridge into the

title ("Valuation Without R"). Vocabulary bridges are built, not emergent.

Summary of hypothesis outcomes.

6. Why silos persist

The measurement explains the phenomenon; the persistence mechanisms are

incentive and infrastructure. Four evidence-grounded mechanisms:

1. Field-local prestige economies. Publication, hiring, and funding

reward vocabulary mastery within a field; mastery is signaled by using

the field's terms, not by translating them. The QNFO seed shows the

organizational version: programs are rewarded for domain depth, and the

taxonomy's bridge subsections exist precisely because nothing in the

ordinary keyword flow crosses boundaries.

1. Vocabulary gatekeeping by venues. Journals, conferences, and

archives curate keyword taxonomies that are field-scoped. A term that

does not appear in the venue's taxonomy is invisible to its search;

authors optimize for the venue's vocabulary, deepening the partition.

1. Translation costs. Establishing that "valuation" in number theory

corresponds to "measurement hierarchy" in epistemology costs

verification effort that the author bears alone and the field does not

reward. The QNFO corpus demonstrates the result: where an author paid

the cost (the "Valuation Without R" title), the bridge is visible;

elsewhere it is not.

1. Path dependence in taxonomy formation. Keyword taxonomies grow by

accretion inside domains; they are rarely audited for cross-domain

overlap, and no one owns the intersection. The seed audit is itself the

exception that tests the rule: it took a computational audit to discover

that an organization's own vocabulary was 99.7% partitional.

7. What the silo costs

The measurable proxies from this study bound the cost:

• Discovery failure. With a 2.8% bridge share, keyword search for a

concept outside the searcher's home domain has a recall ceiling of a few

percent for cross-domain correspondences. The exemplar result (Jaccard

0.0 between two records the program itself cites as semantically linked)

shows the failure at record level: the linkage exists in prose, not in

vocabulary, so it is invisible to lexical retrieval.

• Duplicated discovery. Fields that cannot see each other's vocabulary

cannot see each other's results; the same structural result is

re-derived under new names. The method-bias finding (bridges are 70x

enriched in method vocabulary) sharpens this: disciplines import each

other's tools while remaining blind to each other's structures.

• Convergence delay. Cross-domain consilience — the recognition that

two fields describe one object — is the rate-limiting step for large-scale

synthesis. If vocabulary is 97% partitional, consilience is never

discoverable bottom-up; it requires an explicit act of bridge-building.

8. Consilience infrastructure: bridges are built, not emergent

The constructive response follows directly from the measurement. If shared

vocabulary is rare, method-biased, and author-made, then consilience needs

deliberate vocabulary infrastructure:

1. Bridge vocabularies as first-class artifacts. A bridge vocabulary is

a maintained, versioned mapping between the terms of two or more

domains, with verification notes (which terms correspond, at what level

of structural fidelity). The QNFO taxonomy's bridge subsections are a

primitive version; the corpus's title-visible bridges are the worked

examples of the practice.

1. Taxonomy engineering with an intersection owner. Every domain

taxonomy should have a named owner of the intersection: a person or

process that periodically audits the partition, measures bridge share,

and curates the cross-domain terms. The instrument in this paper is the

audit tool; the seed validation shows it can be run continuously at

organizational scale.

1. Semantic mapping between field vocabularies. For the AI-assisted

synthesis pipeline, the fix is a mapping layer between field-specific

vocabularies — the semantic analogue of a bilingual dictionary — so that

retrieval can find "measurement hierarchy" when asked for "valuation".

The H-SILO-3 result quantifies the need: without the mapping layer,

cross-domain retrieval operates at zero recall on the program's own

cited bridges.

1. Title-visible bridges. The cheapest infrastructure is authorial:

when a paper connects domains, name the connection in the title. The

one visible exemplar (Jaccard 0.333) shows the effect; it is the only

corpus bridge that lexical retrieval can find.

9. Where the premises end

This paper's claims are derived from the measurement pipeline; its premises

end at four named inputs:

• P1 — the vocabulary model. Technical vocabulary is modeled as

stopword-filtered bigrams from titles and abstracts. This is a proxy for

"the terms a field uses": it excludes single-token technical terms, terms

below the frequency threshold, and terms that appear only in bodies. The

QNFO seed, which uses curated keywords, shows the same partitional

structure, but the two pipelines are not identical instruments.

• P2 — the domain partition. The external sample partitions science by

arXiv category. Real disciplines overlap and subdivide; the partition

choice affects the measured quantities (finer partitions raise

partitionality).

• P3 — the general-family list. The Fisher enrichment test depends on

the curated method/pattern list. The list is conservative (method terms

only) and published in full with the instrument; re-running with a

different list changes the odds ratio, not the qualitative result (bridges

are method-concentrated by inspection of all 13 terms).

• P4 — the corpus scope. The QNFO corpus probes are single-organization

evidence: 578 records, 40 resolvable graph pairs, three cited bridges.

The external generalization rests on the six-discipline vocabulary

sample (240 abstracts).

The quantitative claims (partitionality, bridge share, Fisher p, Jaccard

values) are reproduced by the deposited scripts with fixed seeds

(artifacts/verification/). No claim here extends beyond the measured

samples; the mechanisms of Section 6 are explanatory hypotheses grounded in

those measurements plus standard economics of science, not independent

estimates.

10. Limitations and next steps

Limitations: sample breadth (six disciplines, 40 abstracts each — a

snapshot, not a census); the single-token blind spot of the bigram model;

the absence of a temporal dimension (vocabulary silos may be widening or

narrowing over time); and the organization-scale corpus probes.

Next steps: (1) scale the external sample to 30+ disciplines and 1,000+

abstracts per discipline, with author-keyword metadata where available;

(2) add the temporal dimension via arXiv date-banded samples to measure

whether bridge share is changing; (3) build and evaluate the semantic

mapping layer (bilingual-dictionary analogue) on the QNFO corpus, testing

whether mapped retrieval recovers the program's own cited bridges; (4)

publish the bridge-vocabulary audit as a recurring instrument for the QNFO

program (the seed case already runs it).

11. Reproducibility

• scripts/arxivdomainsample.py — external evidence collection

(arXiv API, deterministic extraction; raw evidence in

artifacts/external-search/arxivraw2026-08-24.json).

• scripts/buildqnfodomains.py — canonical taxonomy parser (method of

rq5keywordload.py).

• scripts/terminology_silos.py — measurement toolkit (partitionality,

bridge share, shared core, Fisher exact).

• scripts/hsilo3semanticlinks.py — corpus probes (KG structure,

same-vs-random, exemplar bridges).

• scripts/validateseedvs_published.py — 9/9 validation against the

published audit.

• All outputs and logs: artifacts/verification/. Seeds fixed (42 where

sampling occurs); the extraction itself is deterministic.

References

1. Quni-Gudzinas, R. B. (2026). The Consilience of the QNFO Keyword

Taxonomy: Ultrametric Structure as a Testable Compression Prior.

Zenodo, 10.5281/zenodo.22071421.

1. Quni-Gudzinas, R. B. (2026). The Ultrametric Program: One Structural

Object Across Seven Research Domains, and Its Falsifiable Tests.

Zenodo, 10.5281/zenodo.22073477.

1. Quni-Gudzinas, R. B. (2026). The History and Future of Measurement

Stratigraphy, Number Theory, and Valuation Theory. Zenodo,

10.5281/zenodo.21705220.

1. Quni-Gudzinas, R. B. (2026). Valuation Without R: A Category-Theoretic

Foundation for Finite Measurement. Zenodo, 10.5281/zenodo.21803677.

1. Quni-Gudzinas, R. B. (2026). Ultrametric topology in semantic memory

with invariant cross-ratio stability. Zenodo, 10.5281/zenodo.19564091.

1. QNFO Keyword Taxonomy v1.0 (2026-08-05). docs/QNFO-KEYWORD-TAXONOMY.md,

QNFO/qnfo-research; flat rendering

docs/keyword-taxonomy-source.md.