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

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

references/engagement-frameworks.md

@@ -81,7 +81,7 @@ Pattern: Speak directly to a specific audience
 Analysis of 9,000+ viral posts reveals the science behind what works:
 
 **Pattern Interrupts:**
-- Viral posts contain **2.7x more pattern interrupts** in first two lines
+- Viral posts tend to open with **more pattern interrupts** in the first two lines (multiplier unverified)
 - Pattern interrupts create information gaps that psychologically demand closure
 - Trigger dopamine release and heightened attention
 - Brain's prediction error system activates when expectations disrupted
@@ -347,7 +347,7 @@ Not all engagement is equal. The defensible spine is the **order**, not a fixed
 
 ### First Hour Critical
 - Aim for 15+ engagements in first 60 minutes
-- Respond quickly to early comments (30-minute response = 64% more follow-up comments)
+- Respond quickly to early comments (a 30-minute response tends to earn more follow-up comments — figure unverified)
 - Seed engagement by notifying key connections
 
 ### Comment Strategy