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The New York Unity Project
PublishedNYUP-2026-01·Criminal justice · March 2026

Reducing Recidivism Through Targeted Education and Workforce Programs

An evidence-based case for expanding vocational training and education access inside correctional facilities — with specific legislative proposals, a state-by-state implementation roadmap, and cost-benefit analysis showing net taxpayer savings within six years.

NYUP Research Team·28 pages·47 citations
Key findings
  • States with robust prison education programs see recidivism rates 30–40% below the national average
  • Every $1 invested in prison education saves $4–$5 in future incarceration costs
  • Vocational programs outperform GED programs alone in long-term employment outcomes
  • Federal Pell Grant restoration (2023) created an implementation window most states haven't used
Full text temporarily offline — being re-hosted
Image needed:
photo for the recidivism brief
Suggested: vocational training / classroom setting
Chart needed:
state-by-state recidivism comparison
Suggested source: BJS recidivism data, 2012–2023 cohorts
Graphic needed:
ROI of education programs vs. incarceration cost
Suggested source: the brief's own cost-benefit table
In progressNYUP-2026-02·Electoral reform · Est. 2026

The Case for Ranked-Choice Voting in Federal Elections

A review of the evidence from ranked-choice jurisdictions in the United States and abroad — and a proposal for phased federal adoption starting with primary elections, where polarization is most acute and where RCV has shown the clearest benefits.

NYUP Research Team·Draft in progress
Key arguments
  • First-past-the-post primaries reward candidates who energize the base, not those who can govern broadly
  • Alaska's 2022 adoption of RCV produced two competitive, less polarizing general elections back-to-back
  • RCV can be implemented for primaries by statute — no constitutional amendment required
  • Over 60% of voters in RCV ballot measures have supported adoption
Diagram needed:
how ranked-choice ballot counting works, step by step
Best done as an original graphic — this is the piece readers share
Photo needed:
polling place or ballot imagery (rights-cleared)
Avoid stock that signals either party
In progressNYUP-2026-03·Technology & media · Est. summer 2026

Social Media Algorithmic Transparency Act: A Model Bill

Proposed federal legislation requiring platforms to disclose how their recommendation algorithms amplify politically charged content — and to give users meaningful opt-out tools. Modeled after EU DSA precedents, adapted for the U.S. First Amendment context.

NYUP Research Team·Technology & media
Key arguments
  • Platforms are not neutral conduits — engagement-maximizing curation systematically surfaces outrage
  • Narrowed definitions can separate algorithmic amplification from Section 230's core protections
  • The EU Digital Services Act offers a tested framework adaptable without compelled-speech problems
  • A user opt-out to chronological feeds is a minimal ask with support across the political spectrum
Comparison table needed:
EU DSA provisions vs. the proposed U.S. framework
Two columns, five rows max — keep it readable
Photo needed:
everyday phone use, no visible brand logos
Rights-cleared or original photography