Running pattern detector against the corpus
The dataset shows recipients receiving funding from both the federal grants & contributions stream and Alberta provincial grants — entities sitting at the intersection of two funder pools where program duplication is a real risk that funders can verify against.
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 · general.entity_golden_records with both 'fed' and 'ab' in dataset_sources · ≥ 5 federal and ≥ 5 Alberta source records · combined record count ≥ p90 within the entity's entity_type · confidence ≥ 0.7 · signal = combined record count
The dataset shows Lee, Andy (individual) appears in both federal and Alberta provincial funding streams — 8 federal grant/contribution records and 26 Alberta provincial records, above the 90th percentile for individual of its type. Glassbox surfaces the overlap; the funder verifies whether programs are distinct or duplicative.
Why this may be benign · The recipient business number is missing or a placeholder ('0', all-zeros, '-', 'n/a'): rows were grouped by legal name only, so distinct entities with the same name may be merged (KNOWN-DATA-ISSUES F-6/F-7).
Aggregated from the matches below. Severity bands are calibrated per pattern; counts and dollar totals reflect this snapshot only.