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refactor(linkedin-studio): S31c descriptive-%-scrub — platform-norm percentages asserted as fact -> SSOT

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24 edits / 12 files (+26/-26). Unsourced platform/algorithm/audience percentages reconciled to
SSOT vocabulary (figure/proportion/multiplier unverified). Catalog + new sibling clusters
(64% follow-up x5, wrong-window 70% x4, Stage-2 6-10% x2) + borderlines (70% retention, 70%
mobile) + the ~3% save-worthy straggler (surfaced, not silent). The SSOT-sourced ~70% reach
figure is KEPT; only the wrong window corrected (60min/1h -> first 15-30 min). Sourced/computed
benchmarks kept (Buffer 178%/247%, Socialinsider 11%). KEPT C1: ~45% AI-comment figure (already
hedged correlational/medium-confidence). Gate 81/0/0 exit 0, counts 29/19/26 + v0.5.0.

Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_016qgzo6rxthw7KuxHjn5vyE
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Kjell Tore Guttormsen 2026-06-20 09:54:44 +02:00
commit e17134ee9b
12 changed files with 26 additions and 26 deletions
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10
references/glossary.md
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@ -122,12 +122,12 @@ LinkedIn's mechanism for validating creator authority: professional history, pos
## F
### First-Hour Engagement
Critical window (0-60 minutes post-publication) determining ~70% of a post's total reach. Requires: 5x5x5 pre-posting engagement, immediate response to first comments (within 5 minutes), and continued engagement through 90 minutes.
Critical window — the first 15–30 minutes decide ~70% of a post's total reach (golden window 60–90 min). Requires: 5x5x5 pre-posting engagement, immediate response to first comments (within 5 minutes), and continued engagement through 90 minutes.
**Used in:** `skills/linkedin-studio/SKILL.md`, `references/linkedin-formats.md`, `agents/engagement-coach.md`
### Four-Stage Distribution Model
Sequential post distribution: Stage 1 (0-30s: Quality classifier + profile/topic-relevance validation) → Stage 2 (0-90min: Test to 6-10% of connections) → Stage 3 (1-24h: Extended if velocity good) → Stage 4 (24-72h+: Evergreen circulation).
Sequential post distribution: Stage 1 (0-30s: Quality classifier + profile/topic-relevance validation) → Stage 2 (0-90min: Test to a small slice of connections) → Stage 3 (1-24h: Extended if velocity good) → Stage 4 (24-72h+: Evergreen circulation).
**Used in:** `references/algorithm-signals-reference.md`
@ -150,7 +150,7 @@ Opening 110-140 characters of a post that must work standalone on mobile (before
**Used in:** `references/engagement-frameworks.md`, `skills/linkedin-studio/SKILL.md`, `agents/content-optimizer.md`
### Hook Psychology
Neuroscience-backed engagement: Pattern interrupts trigger prediction error → dopamine release → information gap demanding cognitive closure. Pattern interrupts are 2.7x more common in viral posts. Optimal first line: ~49 characters.
Neuroscience-backed engagement: Pattern interrupts trigger prediction error → dopamine release → information gap demanding cognitive closure. Pattern interrupts are markedly more common in viral posts (multiplier unverified). Optimal first line: ~49 characters.
**Used in:** `references/engagement-frameworks.md`
@ -195,7 +195,7 @@ Three-level connection classification: **Tier 1** (Inner Circle, 5-10 people, da
## P
### Pattern Interrupt
Unexpected statement or data point that breaks normal thought patterns and captures attention. 2.7x more common in viral posts. Examples: contrarian claims, surprising statistics, provocative questions.
Unexpected statement or data point that breaks normal thought patterns and captures attention. Markedly more common in viral posts (multiplier unverified). Examples: contrarian claims, surprising statistics, provocative questions.
**Used in:** `references/engagement-frameworks.md`
@ -213,7 +213,7 @@ Unexpected statement or data point that breaks normal thought patterns and captu
## S
### Save Signal
Highest-value engagement signal — top of the engagement order. A save ≈ 5x a like (≈ 2x a comment) in single-vendor data — directional, not a fixed weight. Saves indicate content worth returning to; posts with saves get 130% higher follow probability. Only ~3% of posts reach save-worthy status.
Highest-value engagement signal — top of the engagement order. A save ≈ 5x a like (≈ 2x a comment) in single-vendor data — directional, not a fixed weight. Saves indicate content worth returning to; posts with saves raise follow probability (saves are a follow-graph signal; figure unverified). Only a small fraction of posts reach save-worthy status.
**Used in:** `references/algorithm-signals-reference.md`, `references/linkedin-growth-playbook-2025-2026.md`