· Private foundation
Iuec Local 1 of NYC Scholarship Fund
Its FY2025 filing reports that it accepted unsolicited grant applications.
What it funded, over time
Every grant placed by its stated purpose and the recipient’s mission, by year, FY2020 to 2025.
Where the money goes
Grants by size, and the largest recipients.
| Recipient | Amount |
|---|---|
| ALEX TITTO PAID TO ADELPHI UNIVERSI | $5,000 |
| JESSICA HALKO PAID TO QUEENS COLLEG | $5,000 |
| CHLOE LOTUFO PAID TO SASSOON ACADEM | $3,500 |
| MAGGIE SWEENEY PAID TO ONONDAGA COM | $3,500 |
| GABRIELLA RAIA PAID TO ST JOHN'S UN | $3,000 |
| JACOB SMITH PAID TO UNIVERSITY OF V | $3,000 |
| KATE MALONEY PAID TO UNIVERSITY OF | $2,500 |
| AVA CATANZARO PAID TO PENN STATE UN | $2,500 |
| MAEVE MURRAY PAID TO THE UNIVERSITY | $2,500 |
| CAYDENCE FERGUSON PAID TO ENDICOTT | $2,500 |
| RYAN FITZGERALD PAID TO RUTGERS-THE | $2,000 |
| RILEY PELLEGRINELLI PAID TO UNIVERS | $2,000 |
| ISABELLA CRISPINO PAID TO FLORIDA S | $2,000 |
| RICHARD RINES PAID TO SUNY ORANGE | $2,000 |
Where the grant dollars land
Across the US and around the world.
Which US states its grants reach
How this is measured
0 grantees tracked through their own filings, 2020–2025.
Each grant beside the grantee’s revenue from every source, year by year.
How this is measured
Counted here: distinct organizations funded across 2020 to 2025, not grant rows in a single year, so this will not match the grant count on the cover. 0 of the 64 grantees resolved in that span have returns of their own we could reconcile.
Each grantee is resolved to its own IRS returns. Association, dated; never a causal claim.
Where the money sits, by cause and then by grantee
Each grantee by its size and the funder’s share of it; top left is load-bearing
Grantee by grantee: a decade per organization, before and after funding
A decade per grantee: revenue shaded, grants as bars, all rows on one timeline.
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Find your warmest path to Iuec Local 1 of NYC Scholarship Fund through trustees and officers whose names appear on both boards. Search for your organization and Plinth traces the shortest route it can evidence.
Each link is a name appearing on two organizations’ public IRS 990 filings, matched on that name and, where the filings support it, on location. A same-state match has geographic support, which is a second matching feature rather than confirmation that the two are one person; a cross-state one is the likeliest to be two people who share a name. Every hop shows its tier, low-confidence and distant paths are held back rather than guessed, and it is worth confirming the person before you use the introduction.
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