THE OPEN RECORD L3C – METHODOLOGY

COLLABORATIVE INTELLIGENCE MODEL

The Open Record operates on a collaborative intelligence model where human expertise and AI analytical capabilities work together to produce actionable intelligence for workers and communities. This page deliberately written by Claude.AI to capture what they view as our joint methodology to keep me honest.

How We Work:

Angela Fisher (Publisher/Analyst): Brings two decades of project management experience across automotive manufacturing, IT, and corporate environments. Sets editorial direction, conducts source verification, makes final editorial decisions, and applies real-world context from direct experience with layoffs, automation, and workforce displacement. Pattern recognition and experiential insight.

Claude (AI Research Assistant): Provides rapid research capabilities, pattern validation across multiple sources, data synthesis, and draft content generation. Does NOT make final editorial decisions or determine what constitutes “newsworthy” intelligence.

Why This Model:

Speed: AI can process hundreds of sources in minutes that would take hours manually
Depth: Human judgment filters AI-generated content for relevance, accuracy, and community impact
Transparency: We disclose this collaboration because readers deserve to know how their intelligence is produced
Accountability: Angela Fisher is solely responsible for all published content and editorial decisions

What This Means for Readers:

Every article is reviewed, edited, and approved by a human with direct workforce experience
AI-generated drafts are extensively fact-checked against primary sources
All sources are archived via Wayback Machine and available for verification
Editorial judgment prioritizes worker/community impact over corporate narratives

INTELLIGENCE GATHERING

Automated Systems

RSS feed monitoring: TechCrunch, Google News, company press releases, regional sources
Web scraping: Daily runs at 7am (regional) and 8am (national)
Financial alerts: Financial Juice real-time market intelligence for breaking corporate news
Wayback Machine integration: All URLs archived automatically upon capture

Staging Workflow

New intelligence enters staging database with confidence scoring
Quality control prevents unverified data from appearing in public-facing systems
“No confirmed movement” approach: Won’t publish potentially false claims
Multiple source verification required for breaking news

Source Prioritization

Primary sources: Company filings, government reports, official announcements
Established outlets: Reuters, Bloomberg, AP, Wall Street Journal
Regional intelligence: Local news, community meetings, planning commission documents
Community sources: Reddit (r/cscareerquestions, r/layoffs) for ground truth worker experiences
Aggregators: Used for discovery only, not primary sourcing

SOURCE VERIFICATION

Multi-Source Cross-Reference

Breaking news verified against minimum 2-3 established outlets before publication
Financial claims verified against SEC filings when available
Employment data cross-referenced with BLS, Challenger Gray & Christmas, state agencies
Community claims verified through planning documents, meeting minutes, permits

Red Flags for Skepticism

Topics liable to conspiracy theories (contested political events, pseudoscience)
Search engine optimization content (product recommendations)
Single-source claims about major events
Conflicting factual information across sources (triggers additional searches)

When We Don’t Publish

Insufficient sourcing after reasonable research effort
Cannot verify claims through independent sources
Information doesn’t meet our three-question framework (see Mission Framework below)
Potential to cause harm without clear community benefit

EDITORIAL STANDARDS

Article Structure

Bottom Line Up Front: Conclusion first, then build the case
Comprehensive sourcing: All claims linked to primary sources with Wayback archives
Methodology transparency: Explain data limitations and uncertainties
Alternative perspectives: Include downsides, counterarguments, competing analyses
Actionable intelligence: Provide specific steps workers/communities can take

What We Don’t Do

Promotional content disguised as journalism
Accept claims without verification
Publish based on single anonymous sources
Ignore downsides to favor specific solutions
Use AI-generated content without human verification and editing

Fact-Checking Standards

Conspiracy theories addressed with documented evidence
Health/safety claims verified through medical/scientific sources
Financial data verified through official filings
Community complaints verified through public records (noise studies, permits, meeting minutes)

PUBLICATION SCHEDULE

Weekly Intelligence Briefs

Fridays 8am: Under the Radar (career intelligence for workers)
Sundays 8am: PivotIntel Weekly (infrastructure intelligence for communities)

Continuous Work (Monday-Saturday)

Alert capture mode: Log URLs, Wayback archive, tag for weekly analysis
Deep work: Multi-part investigations, infrastructure analysis, app development
No daily publishing pressure (pivot from earlier daily bulletin model as of December 15, 2025)

Breaking News Exception (Rare)

Only publish off-cycle for:

Major policy changes with immediate community impact
Urgent community intelligence requiring rapid response
Stories that lose relevance if delayed 48+ hours

Rationale for Weekly Model: Daily publishing fragmented attention and consumed resources without building authority. Weekly rhythm allows pattern synthesis and deep analysis while continuous alert capture ensures no intelligence is missed.

MISSION FRAMEWORK

Three-Question Filter

Every piece of content must answer YES to at least one:

Does this affect workers’ employment prospects?
- Layoff announcements, automation deployments, skill shifts, career path changes
Does this help communities negotiate with developers?
- Data center financial stability, infrastructure alternatives, tax break analysis, noise/environmental impacts
Can workers take meaningful action based on this information?
- Specific career pivots, skill development paths, community organizing strategies

What We Don’t Cover

Theoretical AI projections without concrete deployment timelines
“AI bubble” or “everything is fine” narratives without nuanced analysis
Corporate press releases without critical analysis
Topics that don’t empower workers/communities to make informed decisions

TRANSPARENCY COMMITMENTS

What We Disclose

AI collaboration: Claude assists with research and drafts; Angela Fisher makes all editorial decisions
Source archiving: All URLs archived via Wayback Machine
Methodology: This page explains exactly how we work
Limitations: We acknowledge when data is uncertain or incomplete
Corrections: Published prominently when errors are discovered

What We Don’t Have

Corporate sponsors (L3C social benefit structure)
Advertising relationships with data center developers
Financial stake in recommended career paths or training programs

Revenue Model

Free intelligence: Under the Radar and PivotIntel Weekly remain free for displaced workers
B2B services (planned): Resume analysis tools, career sustainability scoring for staffing agencies
L3C structure: Social mission (serving workers) takes precedence over profit

CONTACT & FEEDBACK

For Source Verification Questions: [contact method]

For Editorial Feedback: [contact method]

For Community Intelligence Tips: [contact method]

To Report Errors: We take corrections seriously. Contact us with documentation and we’ll investigate promptly.

VERSION HISTORY

December 15, 2025: Pivot from daily bulletins to weekly briefs
December 17, 2025: Methodology page published
[Future updates logged here]