Running pattern detector against the corpus
The dataset shows entities receiving substantial federal funding with no recorded business identity — recipients the federal government cannot independently identify.
Aggregated from the matches below. Severity bands are calibrated per pattern; counts and dollar totals reflect this snapshot only.
Glassbox surfaces correlations and observations, not findings of misconduct. Severity is a calibrated read on how strongly the dataset matches the pattern definition — a starting point for review, not a verdict.
The match meets the pattern threshold but the signal is the lower band. Normal review queue.
The match exceeds the routine threshold. Worth a closer look — pull the source rows, walk the citation.
The match is in the highest band of the calibrated scale. Recommend: prioritise for active review.
Severity is computed from the detection signal · recipient_business_number IS NULL · is_amendment = false · total funding ≥ $500K. Severity scales by total $ and departmental spread.
The dataset shows batch report│rapport en lots (Ottawa l Ottawa, QC) received $25.1B across 164 federal agreements from 6 departments, with no recipient business number recorded — pattern consistent with ghost capacity (entity not independently identifiable in the federal corpus).
The dataset shows First Nations Health Authority (West Vancouver, BC) received $8.2B across 4 federal agreements from 3 departments, with no recipient business number recorded — pattern consistent with ghost capacity (entity not independently identifiable in the federal corpus).
Once a match is identified, the dataset suggests these review actions. Glassbox does not prescribe outcomes — these are calibrated options for funders, auditors, and program officers, framed as correlations to investigate.
An entity receiving substantial federal funds without a recorded business number is identifiable only by name string. Verify identity by mapping to corporate / charity registries and adding the BN to the funder's master record.
TRACE's original definition of ghost capacity covers entities with no employees, no addresses, and revenue dominated by government transfers. For these matches, verify capacity (audited financials, employee count, physical presence) before further awards.
What did the entity actually produce for the funding? Pull deliverable reports, site visits, third-party verification. Ghost-capacity is a capacity question, not a fraud claim.
Many matches here are data-entry omissions (BN simply not recorded) rather than substantive ghost capacity. Use this list to clean up master records and tighten future intake.
These are calibrated suggestions, not directives. The dataset shows the pattern; the funder decides the response.
The dataset shows Government of Quebec | Gouvernement du Québec (Quebec city, Quebec | Ville de Québec (Québec), QC) received $7.6B across 3 federal agreements from 3 departments, with no recipient business number recorded — pattern consistent with ghost capacity (entity not independently identifiable in the federal corpus).
The dataset shows CREE NATION GOVERNMENT (NEMASKA | NEMASKA, QC) received $1.4B across 45 federal agreements from 6 departments, with no recipient business number recorded — pattern consistent with ghost capacity (entity not independently identifiable in the federal corpus).
The dataset shows MOHAWK COUNCIL OF AKWESASNE (CORNWALL, ONTARIO|CORNWALL, Ontario, QC) received $766M across 11 federal agreements from 6 departments, with no recipient business number recorded — pattern consistent with ghost capacity (entity not independently identifiable in the federal corpus).
The dataset shows NORWAY HOUSE CREE NATION (Winnipeg, MB) received $700M across 17 federal agreements from 6 departments, with no recipient business number recorded — pattern consistent with ghost capacity (entity not independently identifiable in the federal corpus).