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Prefaces as enrichment: proposal and use cases

Created: 24-07-2026 · Last updated: 24-07-2026 (UC-13 site appendix link)

Status: proposal (research + product framing) · Audience: CDSL / csl-guides maintainers, DH pipeline owners, paper authors
Related: OCR'd prefaces · Preface OCR pipeline · Pref keys ↔ body naming authority · Abbreviations & citations · Issue csl-guides#123

One-line thesis​

Prefaces teach the editor’s map of the Sanskrit library and metalanguage. Joined to body frequency and cross-dictionary tools, they enrich citation resolution, dictionary literacy, lexicographic history, and any controlled vocabulary of works—not more headword text.

What front matter is (and is not)​

LayerRole
Body (csl-orig .txt)Where the lexicon is used: headwords, citations, practice. Human-edited over decades.
Pref / front matter (OCR Markdown)Where the editor declares sources, method, and conventions: title pages, Vorwort, abbreviation legends, addenda.
Print scanAudit trail for OCR fidelity.

Front matter is not a second copy of the dictionary. Full-diff of Vorwort against body is out of scope. Expansions (full titles) stay scan-faithful; used siglum naming aligns toward body when body attests a form (naming authority).

What prefaces can teach​

1. The intended library (vs the used library)​

The abbreviation legend is a declared bibliography. Body counts show what was actually cited; the pref shows what the editor planned to have available.

SignalTeaching
Pref key + expansionCanonical expansion of a siglum as printed
Pref-only / rare / MSWorks known but little used (handschrift, “nach Anführungen…”)
High body countWorking core of the dictionary’s evidence base

The gap (pref vs body) is research data: programmatic vs realized corpus. Typed residual classes (ortho, rare, ocr_key, spacing, true_unused, ambiguous) already capture that typology under pref_only_decompose.

2. Sigla as a controlled vocabulary​

Pref legends are the natural source for:

  • Resolving <ls> citations (what MBh., Çat. Br., Kâtj. Çr. mean)
  • Cross-dictionary same work, different abbreviation maps
  • Separating work sigla from grammar metalanguage (often mixed in one list)

3. Editorial method and period style​

Vorworte encode dictionary identity (thesaurus vs practical glossary vs Buddhist hybrid), dependence on earlier lexica (PWG ↔ PW ↔ MW), house romanization, and explicit limits (“handschriftlich”, “gelegentlich”, named informants). That is lexicographic history, not only lookup UX.

4. Scholarly network and transmission​

Pref pages name editors, collaborators, editions, places (Calc., Bibl. ind., St. Petersburg), and correspondence (“nach Mittheilungen von Schiefner / Weber”). Useful for prosopography and edition genealogy.

5. Grammar metalanguage as a second legend​

MW72, MD, SHS, CAE residual keys are often grammar/meta (abl., s.v., class labels), not text sigla. That teaches how the dictionary teaches morphology in the entry—and why automated body-naming align correctly stops for those keys.

6. Multilingual access​

DE source + EN/RU translations make 19th‑c. German Vorworte usable for students and non-German DH pipelines, while keeping Sanskrit forms, titles, and keys verbatim across languages.

Non-goals (do not force)​

  • Second full lexicon or full-diff Vorwort against body.
  • Bulk expansion rewrite from body (titles stay scan-faithful).
  • Claim that every legend key must appear in body (rare/MS is intentional).
  • Inventing body forms for residual keys with zero body attestation.

Pref teaches intention, convention, and library; body teaches practice and frequency. Enrichment is the join, not collapsing one channel into the other.

Use cases​

Each use case names: actor, pref input, join (if any), output, home / consumer.

UC-1 — Expand <ls> for humans and machines​

  • Actor: site user, API consumer, link-target builder.
  • Pref input: key → expansion from OCR legend.
  • Join: body <ls> tokens + frequency; scan page maps where available.
  • Output: tooltip / panel “MBh. = Mahābhārata …”; ranked “works this dictionary cites most.”
  • Home: csl-guides abbreviations UX; Cologne display; citation graph.

UC-2 — Cross-dictionary work identity​

  • Actor: DH analyst, paper author, abbreviations-comparison maintainer.
  • Pref input: per-dict legend tables (PWG, PW, MW, AP90, …).
  • Join: same underlying work under different sigla and orthographies.
  • Output: crosswalk work_id → {PWG: …, PW: …, MW: …}; comparison tables.
  • Home: abbreviations comparison; kosha / SHARED_CODE-style crosswalks.

UC-3 — Structured legend store (machine-readable)​

  • Actor: pipeline engineer.
  • Pref input: parsed pref Markdown keys + expansions + sources.
  • Join: pref_abbr_crosscheck body counts; residual class from decompose.
  • Output: JSON/TSV per dict: key | expansion | class (work|grammar|meta) | body_count | residual_class | notes.
  • Home: scripts/out/; future feed for site components (not hand-maintained HTML).

UC-4 — Dictionary literacy / “how to read this book”​

  • Actor: student, teacher, first-time CDSL user.
  • Pref input: short Vorwort excerpts + top-N work abbreviations + grammar legend.
  • Join: example body entries using those sigla.
  • Output: per-dictionary “reading pack” page or quiz bank seed.
  • Home: csl-guides user docs; Systema / course companions; entry anatomy.

UC-5 — Pedagogy: grammar metalanguage glossary​

  • Actor: SanGram / Systema curriculum author.
  • Pref input: grammar abbreviation lists (MW72, MD, CAE, SHS).
  • Join: rule modules, treebank tags, quiz item banks.
  • Output: aligned metalanguage glossary (print abbr → modern pedagogical term).
  • Home: SanskritGrammar / SanGram modules; quiz generators in csl-guides.

UC-6 — Citation-graph and corpus attestation enrichment​

  • Actor: data paper / atlas / observatory.
  • Pref input: declared works list.
  • Join: body <ls> census; optional external corpus attestation.
  • Output: “legend coverage of corpus citations”; missing-legend vs unused-legend rates (cf. guides hypotheses GH-4).
  • Home: csl-atlas / csl-observatory; corpus attestation; data paper methods.

UC-7 — Scan deep-links and edition genealogy​

  • Actor: link-target / Cologne frontend maintainer.
  • Pref input: edition titles, place/year, series (Bibl. ind., etc.) in expansions.
  • Join: scan registries, Gild. Bibl. numbers, known PDF loci.
  • Output: candidate link targets for <ls> click-through; edition disambiguation notes.
  • Home: cologne-link-target skill path; per-dict issue trackers.

UC-8 — Orthography and OCR QA fold table​

  • Actor: transcoder / pref-align maintainer.
  • Pref input: residual ortho / ocr_key pairs with body-attested alts (change logs).
  • Join: fold tables in pref_abbr_crosscheck / pref_key_body_align.
  • Output: documented fold examples (j/y, Gṛhj/Gṛhy, Kâtj/Kâty, ḱ/c) without silent invent-a-fold.
  • Home: naming authority; registry scripts/pref_fold_table.json (H1592); sanskrit-util notes where relevant.
  • Status (H1592): shipped — 26 PWG/PW examples + pattern rules; pref_key_body_align.py --self-check.

UC-9 — Correction and addenda harvest​

  • Actor: correction-queue / csl-orig batch maintainer.
  • Pref input: printed addenda/corrigenda and “statt … lies …” notes in front matter.
  • Join: existing change-file pipeline; body locus search.
  • Output: candidate corrections with pref page provenance (not silent body invent).
  • Home: /cologne-correction-queue path; monthly batch-PR.

UC-10 — History of Indology / scholars graph​

  • Actor: IndologyScholars / prosopography.
  • Pref input: named persons, institutions, places, dependency claims in Vorworte.
  • Join: person registers, affiliation timelines, other dictionaries’ front matter.
  • Output: edges “Roth informed X”, “edition used by Y”; talk/paper figures.
  • Home: IndologyScholars datasets; optional ARTICLES methods section.

UC-11 — FAIR / citable front-matter editions​

UC-12 — Publish-safety split surface​

  • Actor: release / Pages operator.
  • Pref input: PD imprint front matter (typically).
  • Join: rights registry for body vs front matter.
  • Output: public pref pages even when bulk body redistribution is constrained.
  • Home: publish-safety-check; GitHub Pages per-dict landings.

UC-13 — Residual analytics for papers and FINDINGS​

  • Actor: paper author, findings maintainer.
  • Pref input: typed residual tables (ALL_pref_only_decompose, align change logs).
  • Join: hit rates by wave; before/after align census.
  • Output: appendix tables (“pref_only is typed, not unused”); honest limitations.
  • Home: Pref residual methods appendix (site); data paper; Uprava FINDINGS; csl-guides CHANGELOG.

UC-14 — RU/EN student-facing copy from Vorwort​

  • Actor: ru-copy / course author.
  • Pref input: translated pref_all.en / .ru (not DE OCR alone).
  • Join: register-sensitive rewrite for learners.
  • Output: short “about this dictionary” blurbs for samskrte / Systema without re-OCRing.
  • Home: Systema content wave; csl-guides intro pages.

Priority map (suggested)​

PriorityUse casesWhy
P0 — already partially builtUC-1, UC-3, UC-8, UC-11, UC-13Tools + OCR editions + naming policy + residual typing exist
P1 — high leverage, clear consumersUC-2, UC-4, UC-6Product + paper + comparison surfaces
P2 — domain depthUC-5, UC-7, UC-9, UC-10Needs more human judgment / external registries
P3 — opportunisticUC-12, UC-14Rights and copy when a release or course needs them

Existing assets (do not rebuild)​

AssetRole
Per-dict prefaces/ + *_all.*OCR + EN/RU editions
pref_abbr_crosscheck.pyPref keys × body census
pref_only_decompose.pyTyped residual
pref_key_body_align.pyBody-attested key rewrites + change logs
scripts/out/Frozen TSV/MD outputs
Site: ocr-prefaces, abbreviations-*, citation-graphUser-facing consumers

Open questions (for a human)​

  1. Should the structured legend store (UC-3) live only in csl-guides scripts/out/, or also ship as site data JSON?
  2. Is UC-2 (cross-dict work identity) a paper primary result, a product feature, or both?
  3. How far should rare/MS pref keys surface in UI (full legend vs “core works only”)?

Provenance​

FieldValue
OriginSession discussion after H1560/H1569/H1571/H1580 pref × body work
ModelGrok 4.5 (grok-4.5)
Policy baselineBody wins for used siglum naming; pref expansions scan-faithful

Dr. Mārcis Gasūns