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.
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 or a placeholder ('0', all-zeros, '-', 'n/a', 'none') · is_amendment = false · total ≥ p90 of ALL recipient totals within the recipient's primary department AND ≥ $500K floor · publisher batch-report rows excluded · signal = total $
The dataset shows SAGAMOK ANISHNAWBEK FIRST NATION (Sagamok Anishnawabek, Ontario, ON) received $202M across 9 federal agreements from 4 departments — above the 90th percentile of recipients funded by Indigenous Services Canada | Services aux Autochtones Canada — with no recipient business number recorded; pattern consistent with ghost capacity (entity not independently identifiable in the federal corpus).
Why this may be benign · Identity rests on the legal name alone (no business number); spelling variants can split one entity into several or merge homonyms (KNOWN-DATA-ISSUES F-6, C-3).
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.
Aggregated from the matches below. Severity bands are calibrated per pattern; counts and dollar totals reflect this snapshot only.
The dataset shows University of Calgary (Calgary | Calgary, ON) received $191M across 72 federal agreements from 9 departments — above the 90th percentile of recipients funded by Social Sciences and Humanities Research Council of Canada | Conseil de recherches en sciences humaines du Canada — with no recipient business number recorded; pattern consistent with ghost capacity (entity not independently identifiable in the federal corpus).
Why this may be benign · Identity rests on the legal name alone (no business number); spelling variants can split one entity into several or merge homonyms (KNOWN-DATA-ISSUES F-6, C-3).
The dataset shows Province of British Columbia (Victoria, BC) received $190M across 9 federal agreements from 3 departments — above the 90th percentile of recipients funded by Agriculture and Agri-Food Canada | Agriculture et Agroalimentaire Canada — with no recipient business number recorded; pattern consistent with ghost capacity (entity not independently identifiable in the federal corpus).
Why this may be benign · Large statutory or formula-driven transfers (health, equalisation, infrastructure envelopes) dominate departmental totals and inflate concentration measures without any procurement decision.
The dataset shows ANIMAKEE WA ZHING #37 (Sioux Narrows | Sioux Narrows, ON) received $190M across 7 federal agreements from 4 departments — above the 90th percentile of recipients funded by Crown-Indigenous Relations and Northern Affairs Canada | Relations Couronne-Autochtones et Affaires du Nord Canada — with no recipient business number recorded; pattern consistent with ghost capacity (entity not independently identifiable in the federal corpus).
Why this may be benign · Identity rests on the legal name alone (no business number); spelling variants can split one entity into several or merge homonyms (KNOWN-DATA-ISSUES F-6, C-3).
The dataset shows Government of British Columbia (Victoria, BC) received $188M across 17 federal agreements from 3 departments — above the 90th percentile of recipients funded by Environment and Climate Change Canada | Environnement et Changement climatique Canada — with no recipient business number recorded; pattern consistent with ghost capacity (entity not independently identifiable in the federal corpus).
Why this may be benign · Large statutory or formula-driven transfers (health, equalisation, infrastructure envelopes) dominate departmental totals and inflate concentration measures without any procurement decision.
The dataset shows ALEXIS NAKOTA SIOUX NATION (GLENEVIS | GLENEVIS, AB) received $187M across 37 federal agreements from 6 departments — above the 90th percentile of recipients funded by Crown-Indigenous Relations and Northern Affairs Canada | Relations Couronne-Autochtones et Affaires du Nord Canada — with no recipient business number recorded; pattern consistent with ghost capacity (entity not independently identifiable in the federal corpus).
Why this may be benign · Identity rests on the legal name alone (no business number); spelling variants can split one entity into several or merge homonyms (KNOWN-DATA-ISSUES F-6, C-3).
The dataset shows FILE HILLS QU'APPELLE TRIBAL COUNCIL INC. (Fort Qu'Appelle | Fort Qu'Appelle, SK) received $185M across 11 federal agreements from 5 departments — above the 90th percentile of recipients funded by Indigenous Services Canada | Services aux Autochtones Canada — with no recipient business number recorded; pattern consistent with ghost capacity (entity not independently identifiable in the federal corpus).
Why this may be benign · Identity rests on the legal name alone (no business number); spelling variants can split one entity into several or merge homonyms (KNOWN-DATA-ISSUES F-6, C-3).
The dataset shows SQUAMISH INDIAN BAND (NORTH VANCOUVER, BC) received $185M across 19 federal agreements from 3 departments — above the 90th percentile of recipients funded by Indigenous Services Canada | Services aux Autochtones Canada — with no recipient business number recorded; pattern consistent with ghost capacity (entity not independently identifiable in the federal corpus).
Why this may be benign · Identity rests on the legal name alone (no business number); spelling variants can split one entity into several or merge homonyms (KNOWN-DATA-ISSUES F-6, C-3).
The dataset shows SAPOTAWEYAK CREE NATION (VIA PELICAN RAPIDS, MB) received $184M across 9 federal agreements from 3 departments — above the 90th percentile of recipients funded by Crown-Indigenous Relations and Northern Affairs Canada | Relations Couronne-Autochtones et Affaires du Nord Canada — with no recipient business number recorded; pattern consistent with ghost capacity (entity not independently identifiable in the federal corpus).
Why this may be benign · Identity rests on the legal name alone (no business number); spelling variants can split one entity into several or merge homonyms (KNOWN-DATA-ISSUES F-6, C-3).
The dataset shows Toronto Catholic District School Board (Toronto, ON, ON) received $184M across 34 federal agreements from 3 departments — above the 90th percentile of recipients funded by Immigration, Refugees and Citizenship Canada | Immigration, Réfugiés et Citoyenneté Canada — with no recipient business number recorded; pattern consistent with ghost capacity (entity not independently identifiable in the federal corpus).
Why this may be benign · Identity rests on the legal name alone (no business number); spelling variants can split one entity into several or merge homonyms (KNOWN-DATA-ISSUES F-6, C-3).
The dataset shows METLAKATLA FIRST NATION (PRINCE RUPERT | PRINCE-RUPERT, BC) received $183M across 18 federal agreements from 5 departments — above the 90th percentile of recipients funded by Crown-Indigenous Relations and Northern Affairs Canada | Relations Couronne-Autochtones et Affaires du Nord Canada — with no recipient business number recorded; pattern consistent with ghost capacity (entity not independently identifiable in the federal corpus).
Why this may be benign · Identity rests on the legal name alone (no business number); spelling variants can split one entity into several or merge homonyms (KNOWN-DATA-ISSUES F-6, C-3).
The dataset shows Province of Saskatchewan (Regina, SK) received $180M across 4 federal agreements from 3 departments — above the 90th percentile of recipients funded by Agriculture and Agri-Food Canada | Agriculture et Agroalimentaire Canada — with no recipient business number recorded; pattern consistent with ghost capacity (entity not independently identifiable in the federal corpus).
Why this may be benign · Large statutory or formula-driven transfers (health, equalisation, infrastructure envelopes) dominate departmental totals and inflate concentration measures without any procurement decision.
The dataset shows DRIFTPILE CREE NATION (DRIFTPILE | DRIFTPILE, AB) received $179M across 15 federal agreements from 4 departments — above the 90th percentile of recipients funded by Crown-Indigenous Relations and Northern Affairs Canada | Relations Couronne-Autochtones et Affaires du Nord Canada — with no recipient business number recorded; pattern consistent with ghost capacity (entity not independently identifiable in the federal corpus).
Why this may be benign · Identity rests on the legal name alone (no business number); spelling variants can split one entity into several or merge homonyms (KNOWN-DATA-ISSUES F-6, C-3).