modified: readme

Adds 12 new primitives across two waves of spec work this session.

meta.* layer (8 primitives) — corpus-snapshot footprint:
  total_messages, corpus_span_days, msg_per_day, active_days,
  activity_density, first_seen_ts, last_seen_ts, fingerprint_confidence.
  Motivated by two actors with identical message counts (53 each) producing
  indistinguishable profiles despite radically different presence shapes
  (0.3-day burst vs 47-day long tail).

Language-aware characterization primitives (4 primitives):
  stylometric.pos_ngram_signature — SimHash over POS bigram frequency vector;
    syntactic skeleton fingerprint that survives full vocabulary paraphrase.
  lexical.dialect_region — BCP-47 free_string (es-CL, es-AR, es-MX, …);
    designed for EYENET integration with INGEOTEC regional-spanish-models.
  lexical.evaluative_morphology_density — diminutive/augmentative/pejorative
    suffix density; stable per-author trait baked into language acquisition.
  lexical.optional_grammar_signature — SimHash over optional-grammar choice
    points (compound/simple past, subjunctive, leísmo, relative pronoun);
    high-reliability Spain vs LatAm discriminator.

Also fixes stale scratchpad.md references throughout (README.md is now the
authority), bumps behave-text to 0.1.3, and updates CHANGELOG.

BEHAVE-TEXT/README.md

@@ -51,7 +51,7 @@ topic = event_topic_for("stylometric.capitalization_habit")
 | `Observation` | Registry-aware subclass of `behave_core.spec.Observation`. Validates `primitive` and `value` against `PRIMITIVE_REGISTRY`. |
 | `Window` | Re-exported from `behave_core`. |
 | `ObservationValue` | Re-exported union type. |
-| `PRIMITIVE_REGISTRY` | `dict[str, ValueTypeSpec]` — the full primitive catalog (35 entries). |
+| `PRIMITIVE_REGISTRY` | `dict[str, ValueTypeSpec]` — the full primitive catalog (47 entries). |
 | `ValueKind` | Enum: `CATEGORICAL`, `NUMERIC`, `HASH`, `ARRAY`, `FREE_STRING`, `BOOL`. |
 | `ValueTypeSpec` | Pydantic model: kind, allowed values, bounds, notes. |
 | `is_known(primitive)` | `bool` — whether a primitive path is registered. |
@@ -64,11 +64,33 @@ present in `behave-shell` but not yet implemented here — `status: planned`.
 
 ## Primitives
 
-35 primitives across 6 categories.
+47 primitives across 7 categories.
 
 ---
 
-### `stylometric.*` — Writing style fingerprints (12 primitives)
+### `meta.*` — Corpus-snapshot footprint (8 primitives)
+
+Meta primitives describe the actor's presence in the corpus window itself —
+how many messages, how long a span, how densely distributed. They are not
+stylometric features; they are the scaffolding that other primitives assume.
+Several primitives (notably `temporal_evolution.lifecycle_phase`) implicitly
+depend on these quantities; `meta.*` makes them first-class so downstream
+attribution engines can access and weight them explicitly.
+
+| Primitive | Kind | Description |
+|---|---|---|
+| `meta.total_messages` | numeric | Raw message count for this actor in the corpus snapshot. Anchor for `msg_per_day` and `fingerprint_confidence`. |
+| `meta.corpus_span_days` | numeric | Wall-clock fractional days between first and last message. First-to-last only — blind to gaps. A 47-day span with 5 active days still yields 47. Recomputable from `first_seen_ts` / `last_seen_ts`. |
+| `meta.msg_per_day` | numeric | `total_messages / corpus_span_days`. Separates bursty visitors (53 msgs / 0.3 days = 53/day) from long-tail lurkers (53 msgs / 47 days = 1.1/day). Undefined when span = 0; extractors emit null/omit rather than divide-by-zero. |
+| `meta.active_days` | numeric | Distinct calendar days (UTC) with ≥1 message. Always ≤ `corpus_span_days`. Distinguishes a periodic visitor (span=47, active=3) from a near-daily regular (span=47, active=40). |
+| `meta.activity_density` | numeric [0,1] | `active_days / corpus_span_days`. 1.0 = present every day of the window. Near-0 = appeared once or twice across a long window. Undefined when span = 0; emit null/omit for single-day actors. |
+| `meta.first_seen_ts` | free_string | ISO 8601 timestamp (UTC offset) of the actor's earliest message. Anchors `corpus_span_days` in absolute time for cross-extraction comparison. |
+| `meta.last_seen_ts` | free_string | ISO 8601 timestamp (UTC offset) of the actor's latest message. See `first_seen_ts`. |
+| `meta.fingerprint_confidence` | categorical | Qualitative reliability of this actor's full fingerprint: `low`, `medium`, `high`. Attribution engines should weight all other observations by this before compositing. Derivation is **extractor-defined** — extractors declare their heuristic in the source label (e.g. `#confidence-v1`). |
+
+---
+
+### `stylometric.*` — Writing style fingerprints (13 primitives)
 
 Stylometric primitives capture the unconscious writing habits that distinguish
 one author from another. The field goes back to the Mosteller-Wallace Federalist
@@ -92,10 +114,11 @@ the Rutify corpus are noted inline where they affect interpretation.
 | `stylometric.function_word_distribution_top200` | hash | 64-bit SimHash over the 200 most common Spanish function words. The wider list reaches into the long tail (rare-but-individual words like `tampoco`, `aunque`, `mientras`) that carry more discriminating signal in short-message corpora. Not yet emitted by v0 prototype — populated in v0.2. |
 | `stylometric.character_ngram_simhash` | hash | 64-bit SimHash over character n-gram frequencies (default n=3), lowercased. Orthogonal to function-word distributions: captures punctuation tics, accent-stripping habits, typo patterns, and idiom fragments that survive paraphrase. Accents are preserved because accent-stripping is itself a stylistic tic. Source label declares n size (e.g. `#char3gram`). |
 | `stylometric.distinctive_vocabulary_signature` | hash | 64-bit SimHash over a TF-IDF-weighted top-K rare-word vector. Captures the author's distinctive lexicon — words they use that other authors in the same corpus do not. Complementary to function-word distributions: where `function_word_*` captures common-word style, this captures individual lexical choice. Requires the full corpus for IDF computation. Source label declares top-K and corpus tag (e.g. `#tfidf-top50`). |
+| `stylometric.pos_ngram_signature` | hash | 64-bit SimHash over a POS n-gram (default bigram) frequency vector. Captures syntactic skeleton independent of vocabulary — an author can change every word and retain the same grammatical fingerprint. Orthogonal to character n-grams and function-word distributions. Tagger-dependent: source label must declare tagger, language model, and n (e.g. `#spacy-es_core_news_sm-bi`). Calibration note: chat-domain text produces tagger noise — weight low until validated on labelled chat corpora. |
 
 ---
 
-### `lexical.*` — Vocabulary and linguistic patterns (8 primitives)
+### `lexical.*` — Vocabulary and linguistic patterns (11 primitives)
 
 Lexical primitives characterize *what* and *how* an actor writes at the word and
 sentence level. Where stylometric primitives fingerprint unconscious micro-habits,
@@ -112,6 +135,9 @@ how questions are formed, register.
 | `lexical.sentence_complexity_class` | categorical | Dominant clause structure. `simple` = single-clause. `compound` = two independent clauses joined by coordinating conjunctions (pero, y, o). `complex` = dependent clauses and subordination (aunque, porque, cuando). Reflects education level and cognitive investment. |
 | `lexical.question_formation_style` | categorical | How questions are formed. `punctuation_only` = question mark without interrogative words ('¿Cuánto?') — very common in Spanish chat. `lexical` = explicit interrogatives (¿qué, cómo, cuándo). `formal` = inverted subject-verb or formal register. |
 | `lexical.imperative_style` | categorical | How commands and requests are framed. `informal_directive` = tú/vos imperative (dame, hazlo). `formal_directive` = usted imperative (hágame el favor). `polite` = conditional/modal softening (¿podría...?). Stable per-author trait in hierarchical contexts. |
+| `lexical.dialect_region` | free_string | Dominant regional variety of the actor's matrix language as a BCP-47 language-region tag (e.g. `es-CL`, `es-AR`, `es-MX`, `es-ES`, `en-US`). Detected from lexical marker density against per-region vocabulary tables. Emit literal `unknown` below confidence threshold. Detection method declared in source label (e.g. `#dialect-markers-v1`). Complementary to `code_switching_matrix_language`, which derives language via switching analysis rather than direct marker lookup. |
+| `lexical.evaluative_morphology_density` | numeric [0,1] | Rate of evaluative morpheme tokens / total tokens. Covers Spanish diminutives (`-ito`/`-ita`), augmentatives (`-ón`/`-ote`), pejoratives (`-ejo`/`-ucho`), and intensives (`-azo`). Heavy diminutive use is characteristic of Mexican/Central American Spanish; River Plate speakers use them significantly less. Stable per-author — baked into language acquisition and hard to consciously suppress. Source label declares morpheme set and tool version (e.g. `#eval-morph-es-v1`). |
+| `lexical.optional_grammar_signature` | hash | 64-bit SimHash over the author's preference probability vector at optional-grammar choice points. For Spanish: compound vs simple past (`he comido` vs `comí` — high-reliability Spain/LatAm discriminator), subjunctive usage rate, leísmo/laísmo/loísmo clitic patterns, and relative pronoun choice (`que` vs `el cual`). Each choice point is a scalar [0,1]; the SimHash is computed over the concatenated vector. Choice-point set is extractor-defined and declared in source label (e.g. `#optgrammar-es-v1`). Requires sufficient corpus volume for stable probabilities — gate on `meta.fingerprint_confidence` before use. |
 
 ---
 
@@ -171,6 +197,49 @@ choose to weight these at zero until field-validated against labeled data.
 
 ---
 
+## Engine implementation notes
+
+### Cross-alphabet fingerprint comparisons are undefined
+
+Several primitives produce hash-based fingerprints by hashing over character or
+syntactic sequences:
+
+- `stylometric.character_ngram_simhash`
+- `stylometric.pos_ngram_signature`
+- `stylometric.function_word_distribution_top50` / `top200`
+- `stylometric.distinctive_vocabulary_signature`
+- `lexical.optional_grammar_signature`
+
+These fingerprints are only **meaningful within a single writing-system boundary**.
+A Latin-script actor (Spanish, English, French, Portuguese) and a Cyrillic-script
+actor (Russian, Bulgarian, Serbian) share zero character n-grams by definition.
+Comparing their `character_ngram_simhash` values produces a Hamming distance that
+is numerically valid but semantically undefined — it does not measure dissimilarity,
+it measures incomparability.
+
+The same applies to any other script boundary: Arabic vs Latin, Hangul vs Hiragana,
+Devanagari vs Cyrillic, and so on.
+
+**Engine rule:** before compositing or comparing any hash-based fingerprint between
+two actors, gate on script/language compatibility. Use `lexical.dialect_region` or
+`lexical.code_switching_matrix_language` to determine whether two actors share a
+writing system. If they do not, treat the fingerprint distance as `undefined` rather
+than as evidence of dissimilarity — do not include it in the similarity composite.
+
+Primitives that are **not** subject to this constraint (safe to compare across
+writing systems without gating):
+
+- All `meta.*` primitives — corpus-footprint metrics are script-agnostic.
+- All `interaction.*` primitives — timing and graph-structure signals are
+  script-agnostic.
+- `stylometric.emoji_usage`, `stylometric.emoji_placement` — Unicode emoji are
+  shared across scripts.
+- `stylometric.capitalization_habit` — only meaningful within scripts that have
+  case; emit `unknown` for caseless scripts (Arabic, CJK, etc.).
+- `network.*`, `temporal_evolution.*` — structural signals, script-agnostic.
+
+---
+
 ## Schema
 
 Machine-readable JSON Schema:

BEHAVE-TEXT/attribution-recipes.md

@@ -2,7 +2,7 @@
 
 # BEHAVE-TEXT Attribution Recipes
 
-> **This document is not part of BEHAVE-TEXT.** BEHAVE-TEXT (`scratchpad.md`) defines the observation taxonomy and emission envelope. It does **not** assert who an actor is, link sessions, or assign profiles. Those are attribution-engine concerns.
+> **This document is not part of BEHAVE-TEXT.** BEHAVE-TEXT (`README.md`) defines the observation taxonomy and emission envelope. It does **not** assert who an actor is, link sessions, or assign profiles. Those are attribution-engine concerns.
 >
 > This document is a **placeholder**. Recipes for the text domain wait for corpus calibration. The Rutify Telegram corpus (forthcoming) will be the labeling ground truth that drives the first concrete profiles.
 

BEHAVE-TEXT/behave_text/spec/primitives.py

@@ -16,7 +16,7 @@ PII discipline notice (carried over from behave-core's envelope module):
   IS text content. Sensors must hash/aggregate before emitting.
 
 Adding a new primitive is a deliberate registry edit. Drift between this file
-and `scratchpad.md` is a bug; v0 keeps the registry hand-written so PR review
+and `README.md` is a bug; v0 keeps the registry hand-written so PR review
 catches drift, v0.x may auto-extract from the markdown if drift becomes a
 maintenance issue.
 
@@ -109,10 +109,71 @@ def _array(of: ValueKind, notes: Optional[str] = None) -> ValueTypeSpec:
 
 # ─── The registry ───────────────────────────────────────────────────────────
 #
-# 28 primitives across 4 layers. Mirrors scratchpad.md row-for-row.
+# 47 primitives across 7 layers. Mirrors README.md row-for-row.
 
 PRIMITIVE_REGISTRY: dict[str, ValueTypeSpec] = {
-    # ── stylometric.* (motor analog — 8) ──────────────────────────────────
+    # ── meta.* (corpus-snapshot footprint — 8) ────────────────────────────
+    "meta.total_messages": _num(
+        min_val=0.0,
+        notes="Raw message count for this actor in the corpus snapshot. Integer in "
+              "practice; stored as float for spec uniformity. Dependency anchor: "
+              "msg_per_day is derived from this; fingerprint_confidence is informed "
+              "by this. Emit before deriving rates.",
+    ),
+    "meta.corpus_span_days": _num(
+        min_val=0.0,
+        notes="Wall-clock duration in fractional days between the actor's earliest "
+              "and latest message in the corpus snapshot. First-to-last only — blind "
+              "to silence in between (a 47-day span with 5 active days still yields "
+              "47). Complement with active_days and activity_density to get presence "
+              "shape. Recomputable from first_seen_ts and last_seen_ts.",
+    ),
+    "meta.msg_per_day": _num(
+        min_val=0.0,
+        notes="total_messages / corpus_span_days. The key rate that separates a "
+              "bursty single-session visitor (53 msgs in 0.3 days → 53/day) from a "
+              "long-tail lurker (53 msgs in 47 days → 1.1/day). Undefined when "
+              "corpus_span_days = 0; extractors should emit null/omit rather than "
+              "divide-by-zero in that edge case.",
+    ),
+    "meta.active_days": _num(
+        min_val=0.0,
+        notes="Count of distinct calendar days (UTC) on which the actor sent at "
+              "least one message. Always ≤ corpus_span_days. An actor with span=47 "
+              "and active_days=3 is a periodic visitor who appears rarely; one with "
+              "span=47 and active_days=40 is a near-daily regular. Use alongside "
+              "activity_density for full presence shape.",
+    ),
+    "meta.activity_density": _num(
+        min_val=0.0, max_val=1.0,
+        notes="active_days / corpus_span_days. Single scalar capturing 'how filled "
+              "is the span?'. 1.0 = present every day of the window. Near-0 = "
+              "appeared once or twice across a long window. Undefined when "
+              "corpus_span_days = 0; emit null/omit for single-day actors.",
+    ),
+    "meta.first_seen_ts": _str(
+        notes="ISO 8601 timestamp (with UTC offset, e.g. '2025-11-03T14:22:07+00:00') "
+              "of the actor's earliest message in the corpus snapshot. Combined with "
+              "last_seen_ts, this anchors corpus_span_days in absolute time so "
+              "observations from different extractions can be compared temporally.",
+    ),
+    "meta.last_seen_ts": _str(
+        notes="ISO 8601 timestamp (with UTC offset, e.g. '2025-12-20T09:11:43+00:00') "
+              "of the actor's latest message in the corpus snapshot. See first_seen_ts.",
+    ),
+    "meta.fingerprint_confidence": _cat(
+        "low", "medium", "high",
+        notes="Qualitative reliability rating for this actor's full fingerprint. "
+              "An attribution engine should weight all other observations from this "
+              "actor proportionally to this value before compositing. Derivation is "
+              "EXTRACTOR-DEFINED — the registry specifies the semantic contract, not "
+              "the formula. Extractors must declare their heuristic in the source "
+              "label (e.g. '#confidence-v1'). Typical inputs: total_messages, "
+              "corpus_span_days, active_days, and any domain-specific thresholds "
+              "the extractor authors have calibrated.",
+    ),
+
+    # ── stylometric.* (motor analog — 13) ─────────────────────────────────
     "stylometric.punctuation_style":          _hash(notes="canonical punctuation-pattern fingerprint"),
     "stylometric.capitalization_habit": _cat(
         "lowercase", "proper", "random_caps", "mixed_i",
@@ -200,8 +261,23 @@ PRIMITIVE_REGISTRY: dict[str, ValueTypeSpec] = {
               "computation, performed once per extraction. Source label declares the "
               "top-K size and corpus tag (e.g. `#tfidf-top50`).",
     ),
+    "stylometric.pos_ngram_signature": _hash(
+        notes="64-bit simhash over a POS n-gram (default bigram) frequency vector "
+              "from the author's text corpus. Captures syntactic skeleton independent "
+              "of vocabulary — an author can change every word they use and still "
+              "retain the same POS-bigram fingerprint. ORTHOGONAL to character_ngram "
+              "and function_word distributions: those capture surface form, this "
+              "captures grammatical structure. Example signal: consistent ADJ-NOUN vs "
+              "NOUN-ADJ ordering in Spanish, habitual ADV-VERB pre-position. "
+              "TAGGER-DEPENDENT: source label MUST declare the tagger, language model, "
+              "and n value (e.g. `#spacy-es_core_news_sm-bi` for spaCy Spanish "
+              "small model, bigrams). Calibration note: chat-domain text is noisy — "
+              "abbreviations, misspellings, and code-switching cause tagger errors "
+              "that introduce fingerprint noise. Engines should weight low until "
+              "calibrated against labelled chat corpora.",
+    ),
 
-    # ── lexical.* (cognitive analog — 8) ──────────────────────────────────
+    # ── lexical.* (cognitive analog — 11) ─────────────────────────────────
     "lexical.vocabulary_richness":            _num(
         min_val=0.0, max_val=1.0,
         notes="Moving-Average Type-Token Ratio (MATTR) over a sliding window "
@@ -242,6 +318,52 @@ PRIMITIVE_REGISTRY: dict[str, ValueTypeSpec] = {
               "market contexts where hierarchical and peer relationships are expressed "
               "through register choice.",
     ),
+    "lexical.dialect_region": _str(
+        notes="Dominant regional variety of the actor's matrix language, expressed as "
+              "a BCP-47 language-region tag (e.g. `es-CL`, `es-AR`, `es-MX`, `es-ES`, "
+              "`en-US`). Detected from lexical marker density against per-region "
+              "vocabulary tables; detection method and marker set version declared in "
+              "source label (e.g. `#dialect-markers-v1`). Emit the literal string "
+              "`unknown` when the extractor falls below its confidence threshold — do "
+              "not omit the observation, so downstream engines can distinguish "
+              "'undetected' from 'not extracted'. Language-agnostic in concept; the "
+              "marker vocabulary is language-specific. COMPLEMENTARY to "
+              "lexical.code_switching_matrix_language, which captures the dominant "
+              "language via switching analysis rather than direct regional-marker lookup.",
+    ),
+    "lexical.evaluative_morphology_density": _num(
+        min_val=0.0, max_val=1.0,
+        notes="Rate of evaluative morpheme tokens / total tokens. Evaluative morphology "
+              "encompasses suffixes that add expressive/emotional loading to a stem: "
+              "diminutives (`-ito`/`-ita`/`-cito`/`-cita` — affection, minimization, "
+              "softening), augmentatives (`-ón`/`-ona`/`-ote`/`-ota` — intensification), "
+              "pejoratives (`-ejo`/`-eja`/`-ucho`/`-ucha` — contempt), and intensives "
+              "(`-azo`/`-aza` — force or admiration by context). Heavy diminutive use "
+              "is characteristic of Mexican and Central American Spanish; River Plate "
+              "speakers use them significantly less. The density is stable per-author "
+              "and hard to consciously suppress — it is baked into language acquisition. "
+              "Language-agnostic in concept; detection (suffix rules or morphological "
+              "analyser) is language-specific. Source label declares the morpheme set "
+              "and tool version (e.g. `#eval-morph-es-v1`).",
+    ),
+    "lexical.optional_grammar_signature": _hash(
+        notes="64-bit simhash over a vector of the author's preference probabilities "
+              "at optional-grammar choice points — positions where the language offers "
+              "multiple grammatically correct options and individual authors make stable "
+              "idiosyncratic choices. For Spanish: compound past vs simple past ratio "
+              "(`he comido` vs `comí` — Spain strongly prefers compound for recent "
+              "actions; Latin America almost universally uses simple past, making this "
+              "a high-reliability Spain/LatAm discriminator), subjunctive usage rate "
+              "(avoidance correlates with informal register or non-native acquisition), "
+              "leísmo/laísmo/loísmo clitic patterns (`le vi` vs `lo vi` for masculine "
+              "accusative — leísmo is characteristic of Castilian Spain), and relative "
+              "pronoun choice (`que` vs `el cual/la cual` — register marker). Each "
+              "choice point is a scalar [0,1] probability; the simhash is computed over "
+              "the concatenated vector. EXTRACTOR-DEFINED: choice-point set declared in "
+              "source label (e.g. `#optgrammar-es-v1`). Requires sufficient corpus "
+              "volume for stable probability estimates — thin corpora produce noisy "
+              "hashes; engines should gate on meta.fingerprint_confidence before use.",
+    ),
 
     # ── temporal_evolution.* (lifecycle / change-over-time — 1) ───────────
     "temporal_evolution.lifecycle_phase": _cat(

BEHAVE-TEXT/pyproject.toml

@@ -4,7 +4,7 @@ build-backend = "setuptools.build_meta"
 
 [project]
 name = "behave-text"
-version = "0.1.1"
+version = "0.1.3"
 description = "BEHAVE-TEXT — text/messaging-domain behavioral observation registry, layered on behave-core"
 readme = "README.md"
 requires-python = ">=3.11"

BEHAVE-TEXT/tests/test_primitives.py

@@ -1,9 +1,9 @@
 # SPDX-License-Identifier: GPL-3.0-or-later
 """Registry coverage tests for BEHAVE-TEXT.
 
-Asserts that every primitive listed in scratchpad.md's tables has exactly one
+Asserts that every primitive listed in README.md's tables has exactly one
 entry in PRIMITIVE_REGISTRY. Drift-detector — failing this test means
-scratchpad.md and the registry have diverged.
+README.md and the registry have diverged.
 """
 
 from __future__ import annotations
@@ -13,9 +13,18 @@ from pathlib import Path
 
 from behave_text.spec import PRIMITIVE_REGISTRY, ValueKind
 
-# Primitive paths expected by scratchpad.md (hand-extracted; v0).
+# Primitive paths expected by README.md (hand-extracted; v0).
 EXPECTED_PRIMITIVES = {
-    # stylometric.* (motor analog — 8)
+    # meta.* (corpus-snapshot footprint — 8)
+    "meta.total_messages",
+    "meta.corpus_span_days",
+    "meta.msg_per_day",
+    "meta.active_days",
+    "meta.activity_density",
+    "meta.first_seen_ts",
+    "meta.last_seen_ts",
+    "meta.fingerprint_confidence",
+    # stylometric.* (motor analog — 13)
     "stylometric.punctuation_style",
     "stylometric.capitalization_habit",
     "stylometric.emoji_usage",
@@ -28,7 +37,8 @@ EXPECTED_PRIMITIVES = {
     "stylometric.function_word_distribution_top200",
     "stylometric.character_ngram_simhash",
     "stylometric.distinctive_vocabulary_signature",
-    # lexical.* (cognitive analog — 8)
+    "stylometric.pos_ngram_signature",
+    # lexical.* (cognitive analog — 11)
     "lexical.vocabulary_richness",
     "lexical.slang_density",
     "lexical.code_switching_rate",
@@ -37,6 +47,9 @@ EXPECTED_PRIMITIVES = {
     "lexical.sentence_complexity_class",
     "lexical.question_formation_style",
     "lexical.imperative_style",
+    "lexical.dialect_region",
+    "lexical.evaluative_morphology_density",
+    "lexical.optional_grammar_signature",
     # temporal_evolution.* (lifecycle/change-over-time — 1, added v0.2)
     "temporal_evolution.lifecycle_phase",
     # network.* (governance/role-shape — 2, added v0.3)

CHANGELOG.md

@@ -6,6 +6,45 @@ Versions follow [Semantic Versioning](https://semver.org/spec/v2.0.0.html).
 
 ---
 
+## [behave-text 0.1.3] — 2026-05-23
+
+### behave-text
+
+#### Added
+- `stylometric.pos_ngram_signature` — 64-bit SimHash over POS n-gram (default bigram)
+  frequency vector. Captures syntactic skeleton independent of vocabulary. Tagger-dependent;
+  source label must declare tagger + model + n. Calibration note: noisy on chat-domain text,
+  weight low until validated.
+- `lexical.dialect_region` — BCP-47 language-region free_string (`es-CL`, `es-AR`, `es-MX`,
+  `es-ES`, `en-US`, etc.) for the actor's dominant regional variety, detected from lexical
+  marker density. Emit `unknown` below confidence threshold. Designed for EYENET integration
+  with INGEOTEC `regional-spanish-models` vocabulary tables (MIT).
+- `lexical.evaluative_morphology_density` — numeric [0,1] rate of evaluative morpheme tokens
+  (diminutives, augmentatives, pejoratives, intensives) per total tokens. Stable per-author
+  trait baked into language acquisition; strong Spain/LatAm regional discriminator.
+- `lexical.optional_grammar_signature` — 64-bit SimHash over author preference probabilities
+  at optional-grammar choice points (for Spanish: compound vs simple past, subjunctive usage,
+  leísmo/laísmo/loísmo, relative pronoun choice). Choice-point set is extractor-defined and
+  declared in source label.
+
+---
+
+## [behave-text 0.1.2] — 2026-05-23
+
+### behave-text
+
+#### Added
+- `meta.*` layer — 8 new corpus-snapshot primitives: `total_messages`, `corpus_span_days`,
+  `msg_per_day`, `active_days`, `activity_density`, `first_seen_ts`, `last_seen_ts`,
+  `fingerprint_confidence`. Fills the gap between actors with identical message counts but
+  radically different presence shapes (bursty single-session vs long-tail lurker).
+
+#### Fixed
+- Stale `scratchpad.md` references in `primitives.py` docstring, `tests/test_primitives.py`
+  docstring, and `attribution-recipes.md` — `README.md` is now the authority.
+
+---
+
 ## [0.1.0] — 2026-05-17
 
 Initial public release of all three packages.