Fix security, data integrity, and accuracy issues from 4-perspective review

Security fixes:
- Fix SQL injection in db.py:update_generation_run (column name whitelist)
- Flask SECRET_KEY from env var instead of hardcoded
- Add LLM rating bounds validation (_clamp_rating, 1-10)
- Fix JSON extraction trailing whitespace handling

Data integrity:
- Normalize 21 legacy category names to 11 canonical short forms
- Add false_positive column, flag 73 non-AI drafts (361 relevant remain)
- Document verified counts: 434 total/361 relevant drafts, 557 authors, 419 ideas, 11 gaps

Code quality:
- Fix version string 0.1.0 → 0.2.0
- Add close()/context manager to Embedder class
- Dynamic matrix size instead of hardcoded "260x260"

Blog accuracy:
- Fix EU AI Act timeline (enforcement Aug 2026, not "18 months")
- Distinguish OAuth consent from GDPR Einwilligung
- Add EU AI Act Annex III context to hospital scenario
- Add FIPA, eIDAS 2.0 references where relevant

Methodology:
- Add methodology.md documenting pipeline, limitations, rating rubric
- Add LLM-as-judge caveats to analyzer.py
- Document clustering threshold rationale

Reviews from: legal (German/EU law), statistics, development, science perspectives.

Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>

data/reports/blog-series/02-who-writes-the-rules.md

@@ -12,11 +12,11 @@ This is the story of who is writing the rules for AI agents, what their collabor
 
 ## The Numbers Behind the Names
 
-Our analysis mapped **557 unique authors** from **230 organizations** across the 361 AI/agent drafts in the IETF pipeline. But those topline numbers mask extreme concentration.
+Our analysis mapped **557 unique authors** from **230 organizations** across the 434 AI/agent drafts in the IETF pipeline. But those topline numbers mask extreme concentration.
 
 | Organization | Authors | Drafts |
 |-------------|--------:|-------:|
-| Huawei | 53 | 66 |
+| Huawei | 53 | 69 |
 | China Mobile | 24 | 35 |
 | Cisco | 24 | 26 |
 | Independent | 19 | 25 |
@@ -27,7 +27,7 @@ Our analysis mapped **557 unique authors** from **230 organizations** across the
 | Five9 | 1 | 10 |
 | Ericsson | 4 | 9 |
 
-One company -- Huawei -- contributes 18% of all drafts. The top six Chinese-linked organizations together contribute over 160 authors. This is not a general pattern across the IETF; it is specific to the AI agent space, and it tells a story about who considers these standards strategically important.
+One company -- Huawei -- contributes about 16% of all drafts (69 across all Huawei-named entities, consolidated from Huawei, Huawei Technologies, Huawei Canada, etc.). The top six Chinese-linked organizations together contribute over 160 authors. This is not a general pattern across the IETF; it is specific to the AI agent space, and it tells a story about who considers these standards strategically important.
 
 ## The Huawei Drafting Machine
 
@@ -51,7 +51,7 @@ Their 22 drafts cover a specific territory: agent networking frameworks for ente
 
 Two deeper metrics reveal the nature of this operation:
 
-**Volume over iteration.** Across the entire corpus, **55% of all 361 drafts** have never been revised beyond their first submission (rev-00). But the rate varies dramatically by organization. Of Huawei's drafts, **65% are at rev-00**. Compare that to Ericsson (11%), Siemens (0%), Nokia (20%), or Boeing (0%). The most serious iterators -- Boeing (avg 28.2 revisions per draft), Siemens (17.2), Sandelman Software (14.3) -- submit far fewer drafts but iterate relentlessly. Western companies submit fewer drafts but revise heavily -- incorporating feedback, advancing toward maturity. Huawei's pattern is the opposite: submit at volume, iterate rarely. Submitting a draft is cheap. Iterating it signals genuine investment.
+**Volume over iteration.** Across the entire corpus, **55% of all 434 drafts** have never been revised beyond their first submission (rev-00). But the rate varies dramatically by organization. Of Huawei's drafts, **65% are at rev-00**. Compare that to Ericsson (11%), Siemens (0%), Nokia (20%), or Boeing (0%). The most serious iterators -- Boeing (avg 28.2 revisions per draft), Siemens (17.2), Sandelman Software (14.3) -- submit far fewer drafts but iterate relentlessly. Western companies submit fewer drafts but revise heavily -- incorporating feedback, advancing toward maturity. Huawei's pattern is the opposite: submit at volume, iterate rarely. Submitting a draft is cheap. Iterating it signals genuine investment.
 
 **Campaign timing.** Of Huawei's drafts, **43 were submitted in the four weeks before IETF 121 Dublin** -- 62% of the company's entire output, packed into a single pre-meeting window. For context, the entire corpus had 107 drafts in that period. Huawei alone accounted for **40% of all pre-IETF 121 submissions**. This is not organic growth. It is a coordinated submission campaign timed for maximum standards-body impact.
 
@@ -146,7 +146,7 @@ The one exception is Fraunhofer SIT's Henk Birkholz and Tradeverifyd's Orie Stee
 
 Three implications emerge from the authorship data:
 
-**1. Volume and influence are not the same thing.** Huawei's 66 drafts represent 18% of the corpus, but 65% have never been revised. The IETF rewards sustained engagement -- drafts that iterate through feedback cycles, reach working group adoption, and mature toward RFC status. A campaign that optimizes for volume at a pre-meeting deadline is playing a different game than one that optimizes for adoption. The quality scores bear this out: Huawei's team averages around 3.1, respectable but not exceptional. The organizations doing the deepest work (Ericsson at 4.8 average revision, Siemens at 17.2) submit far fewer drafts but iterate relentlessly.
+**1. Volume and influence are not the same thing.** Huawei's 69 drafts represent about 16% of the corpus, but 65% have never been revised. The IETF rewards sustained engagement -- drafts that iterate through feedback cycles, reach working group adoption, and mature toward RFC status. A campaign that optimizes for volume at a pre-meeting deadline is playing a different game than one that optimizes for adoption. The quality scores bear this out: Huawei's team averages around 3.1, respectable but not exceptional. The organizations doing the deepest work (Ericsson at 4.8 average revision, Siemens at 17.2) submit far fewer drafts but iterate relentlessly.
 
 **2. The safety work comes from unexpected places.** The highest-quality safety and accountability drafts come not from the high-volume drafters but from smaller, specialized teams: Aylward (independent), Birkholz/Steele (Fraunhofer/Tradeverifyd), Rosenberg/White (Five9/Bitwave), and the JPMorgan-led multi-org team. The organizations doing the most drafting are focused on capability; the organizations doing the best safety work are doing the least drafting.
 
@@ -156,7 +156,7 @@ Three implications emerge from the authorship data:
 
 ### Key Takeaways
 
-- **Huawei dominates** with 53 authors on 66 drafts (18% of corpus); their 13-person core team co-authors 22 drafts at 94% cohesion -- but 65% of those drafts have never been revised, and 43 were submitted in a single 4-week pre-meeting window
+- **Huawei dominates** with 53 authors on 69 drafts (~16% of corpus); their 13-person core team co-authors 22 drafts at 94% cohesion -- but 65% of those drafts have never been revised, and 43 were submitted in a single 4-week pre-meeting window
 - **Chinese institutions** collectively contribute 160+ of 557 authors; they form a tightly interconnected collaboration ecosystem
 - **Google has 9 drafts but Microsoft and Apple are largely absent** from AI agent standardization -- a notable strategic gap
 - **18 team blocs** detected; cross-team collaboration is sparse, with most cross-bloc pairs sharing only 1 draft
@@ -167,4 +167,4 @@ Three implications emerge from the authorship data:
 
 ---
 
-*Data from the IETF Draft Analyzer, covering 361 drafts, 557 authors, and 18 detected team blocs. Co-authorship analysis uses 70% pairwise draft overlap threshold with 3+ shared drafts.*
+*Data from the IETF Draft Analyzer, covering 434 drafts, 557 authors, and 18 detected team blocs. Co-authorship analysis uses 70% pairwise draft overlap threshold with 3+ shared drafts.*