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 METIS PROVINCIAL COUNCIL OF BRITISH COLUMBIA (SURREY | SURREY, BC) received $78M across 11 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).
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 SANDY LAKE FIRST NATION (Sandy Lake | Sandy Lake, ON) received $78M across 12 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 City of Ottawa|Ville d'Ottawa (OTTAWA|OTTAWA, ON) received $78M across 17 federal agreements from 3 departments — above the 90th percentile of recipients funded by Department of Housing, Infrastructure and Communities | Ministère du Logement, de l’Infrastructure et des Collectivités — 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 Université Laval (Sainte-Foy, QC) received $77M across 91 federal agreements from 16 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 MICMACS OF GESGAPEGIAG (MARIA, QC) received $77M across 13 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 ALGONQUINS OF BARRIERE LAKE (Rapid Lake, QC) received $77M across 10 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).
The dataset shows City of Hamilton (Hamilton, ON, ON) received $77M across 16 federal agreements from 6 departments — above the 90th percentile of recipients funded by Innovation, Science and Economic Development Canada | Innovation, Sciences et Développement économique 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 GWICH'IN TRIBAL COUNCIL (Inuvik, Northwest Territories|Inuvik, Territoires du Nord-Ouest, NT) received $76M 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 ATHABASCA DENE EDUCATION CORP. (Prince Albert | Prince Albert, SK) received $76M across 9 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 ESGENOOPETITJ FIRST NATION (BURNT CHURCH, NEW BRUNSWICK|BURNT CHURCH, Nouveau-Brunswick, NB) received $76M across 36 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).
The dataset shows CHIPEWYAN PRAIRIE FIRST NATION (CHARD | CHARD, AB) received $75M across 10 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 University of Alberta (EDMONTON|EDMONTON, AB) received $75M across 59 federal agreements from 8 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 McGill University (Ste-Anne-de-Bellevue, QC) received $75M across 50 federal agreements from 13 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 METIS NATION OF ONTARIO (TORONTO, ON) received $74M across 45 federal agreements from 7 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 NIISAACHEWAN ANISHINAABE NATION (KENORA, ON) received $74M across 10 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 Le Gouvernement du Québec (Québec, QC) received $73M across 3 federal agreements from 3 departments — above the 90th percentile of recipients funded by National Defence | Défense nationale — 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 KWADACHA NATION (Prince George | Prince George, BC) received $72M across 7 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).
The dataset shows SEINE RIVER FIRST NATION (Mine Centre | Mine Centre, ON) received $72M across 8 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 PIKANGIKUM FIRST NATION (PIKANGIKUM | PIKANGIKUM, ON) received $72M across 13 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).
The dataset shows City of Surrey|Ville de Surrey (SURREY|SURREY, BC) received $71M across 38 federal agreements from 3 departments — above the 90th percentile of recipients funded by Transport Canada | Transports 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).