VERIFYIndependent Reproduction of Findings

# Download IRS 990-N e-Postcard data and search for DCA
$ curl -sL https://apps.irs.gov/pub/epostcard/data-download-epostcard.zip | \
  python3 -c "import zipfile,sys,io; z=zipfile.ZipFile(io.BytesIO(sys.stdin.buffer.read())); \
  [print(l) for l in z.open(z.namelist()[0]).read().decode().split('\n') \
  if 'DIGITAL CHILDHOOD' in l.upper()]"

# Search IRS Business Master File for DCI
$ curl -s https://www.irs.gov/pub/irs-soi/eo2.csv | grep -i "digital childhood"

# Search ALL four IRS BMF extracts for "digital childhood"
# Only DCI should appear (in eo2). DCA will not.
$ for f in eo1 eo2 eo3 eo4; do
    echo "--- $f.csv ---"
    curl -s "https://www.irs.gov/pub/irs-soi/$f.csv" | grep -i "digital childhood"
  done

# Search IRS BMF for NCOSEAction by EIN
# NOTE: IRS periodically moves records between regional files.
# If eo2 returns nothing, try eo1, eo3, eo4.
$ curl -s https://www.irs.gov/pub/irs-soi/eo2.csv | grep "881180705"

# Confirm Network for Good in IRS BMF
$ curl -s https://www.irs.gov/pub/irs-soi/eo2.csv | grep "680480736"

# Fetch DCA donation page and extract project ID from URLs
$ curl -sL https://digitalchildhoodalliance.networkforgood.com/ | \
  grep -o 'projects/258136[^"'"'"']*' | head -1

# 990-N shows tax year 2024 (fields 6-7: 01-01-2024 to 12-31-2024)
$ curl -sL https://apps.irs.gov/pub/epostcard/data-download-epostcard.zip | \
  python3 -c "import zipfile,sys,io; z=zipfile.ZipFile(io.BytesIO(sys.stdin.buffer.read())); \
  [print(l) for l in z.open(z.namelist()[0]).read().decode().split('\n') \
  if 'DIGITAL CHILDHOOD' in l.upper()]"

# WHOIS shows domain registered December 2024
$ whois digitalchildhoodalliance.org | grep -i "creation date"

# Query Colorado lobbying disclosures: Headwaters + Meta/Facebook
$ curl -s "https://data.colorado.gov/resource/dxfk-9ifj.json?\$where=lobbyistfirmname%20like%20'%25Headwaters%25'%20AND%20(clientname%20like%20'%25Meta%25'%20OR%20clientname%20like%20'%25Facebook%25')&\$limit=500&\$order=dateincomereceived" | \
  python3 -c "import json,sys; d=json.load(sys.stdin); \
  [print(f'{r[\"fiscalyear\"]} {r[\"reportmonth\"]:>10} | \${float(r[\"incomeamount\"]):>8,.0f} | {r[\"clientname\"]}') for r in d]; \
  print(f'\nTOTAL: \${sum(float(r[\"incomeamount\"]) for r in d):,.0f} ({len(d)} payments)')"

# ConnectSafely 990 filing history via ProPublica API (no auth required)
$ curl -s "https://projects.propublica.org/nonprofits/api/v2/organizations/473168168.json" | \
  python3 -c "import json,sys; d=json.load(sys.stdin); \
  [print(f'TY{f[\"tax_prd_yr\"]} | contributions: \${f[\"totcntrbgfts\"]:>10,} | revenue: \${f[\"totrevenue\"]:>10,} | prog svc rev: \${f[\"totprgmrevnue\"]:>10,}') \
  for f in d['filings_with_data']]"

# First 5 Wayback Machine captures of DCA domain
$ curl -sL "https://web.archive.org/cdx/search/cdx?url=digitalchildhoodalliance.org&output=text&limit=5&fl=timestamp,original,statuscode"

# All Meta/Facebook bill positions filed with Colorado SOS
# Deduplicates by bill, showing one position per bill-session
$ curl -s "https://data.colorado.gov/resource/sche-yqzf.json?\$where=clientname%20like%20'%25Meta%25'%20OR%20clientname%20like%20'%25Facebook%25'&\$limit=500&\$order=fiscalyear,billnumber" | \
  python3 -c "import json,sys; d=json.load(sys.stdin); seen={}
for r in d:
  k=r['fiscalyear']+r['billnumber']; p=r['position'].split(' ')[0]
  if k not in seen or p in ('Amending','Opposing','Supporting'): seen[k]=(r,p)
[print(f'{r[\"fiscalyear\"]:>12} | {r[\"billnumber\"]:>12} | {p:>12} | {r[\"billinformation\"]}') for r,p in seen.values()]"

# Query Senate LDA for all Meta quarterly lobbying filings in 2025
$ curl -s "https://lda.senate.gov/api/v1/filings/?client_name=Meta+Platforms&filing_year=2025&filing_type=Q&format=json&page_size=100" | \
  python3 -c "
import json, sys
d = json.load(sys.stdin)
results = d['results']
total = 0
for r in sorted(results, key=lambda x: (x['filing_period'], x['registrant']['name'])):
    amt = float(r.get('income') or r.get('expenses') or 0)
    total += amt
    print(f'{r[\"filing_period\"]} | \${amt:>10,.0f} | {r[\"registrant\"][\"name\"][:50]}')
print(f'\nTOTAL: \${total:,.0f} across {len(results)} quarterly filings')
"

# NVF 990 filing history via ProPublica API (no auth required)
$ curl -s "https://projects.propublica.org/nonprofits/api/v2/organizations/205806345.json" | \
  python3 -c "
import json, sys
d = json.load(sys.stdin)
print(f'Organization: {d[\"organization\"][\"name\"]}')
print(f'EIN: {d[\"organization\"][\"ein\"]}')
print(f'Subsection: 501(c)({d[\"organization\"][\"subsection_code\"]})\n')
for f in d['filings_with_data']:
    print(f'TY{f[\"tax_prd_yr\"]} | revenue: \${f[\"totrevenue\"]:>14,} | contributions: \${f[\"totcntrbgfts\"]:>14,}')
"

# NCOSE 990 filing history via ProPublica API (no auth required)
$ curl -s "https://projects.propublica.org/nonprofits/api/v2/organizations/132608326.json" | \
  python3 -c "import json,sys; d=json.load(sys.stdin); \
  print(f'Organization: {d[\"organization\"][\"name\"]}'); \
  print(f'EIN: {d[\"organization\"][\"ein\"]}\n'); \
  [print(f'TY{f[\"tax_prd_yr\"]} | revenue: \${f[\"totrevenue\"]:>12,} | expenses: \${f[\"totfuncexpns\"]:>12,}') \
  for f in d['filings_with_data']]"

# Query all four Arabella-managed funds via ProPublica
$ for ein in 205806345 264486735 473522162 473681860; do
    curl -s "https://projects.propublica.org/nonprofits/api/v2/organizations/$ein.json" | \
      python3 -c "import json,sys; d=json.load(sys.stdin); o=d['organization']; \
      f=d['filings_with_data'][0]; \
      print(f'{o[\"ein\"]:>10} | {o[\"name\"]:<30} | 501(c)({o[\"subsection_code\"]}) | TY{f[\"tax_prd_yr\"]} revenue: \${f[\"totrevenue\"]:>14,}')"
  done

# Search ALL four IRS BMF extracts for NCOSE-named entities
$ for f in eo1 eo2 eo3 eo4; do
    echo "--- $f.csv ---"
    curl -s "https://www.irs.gov/pub/irs-soi/$f.csv" | grep -i "NATIONAL CENTER ON SEXUAL"
  done

# Extract child/youth safety language from Meta's 2025 LD-2 filings
$ curl -s "https://lda.senate.gov/api/v1/filings/?client_name=Meta+Platforms&filing_year=2025&format=json&page_size=100" | \
  python3 -c "
import json, sys
d = json.load(sys.stdin)
seen = set()
for r in d['results']:
    for a in r.get('lobbying_activities', []):
        desc = a.get('description', '')
        key = r['registrant']['name'] + desc
        if key in seen: continue
        seen.add(key)
        parts = desc.split(';') if ';' in desc else [desc]
        hits = [p.strip() for p in parts
                if any(w in p.lower() for w in ['child', 'youth', 'parent'])]
        if hits:
            print(f'{r[\"filing_period_display\"][:12]:12} | {r[\"registrant\"][\"name\"][:45]}')
            for h in hits: print(f'  \u2192 {h}')
            print()
"