Tag: federal overreach

Horizon Accord | Minnesota | Cultural Seeding | Institutional Control | Machine Learning

Minnesota Is the Terrain

How environmental punishment replaces direct political attack.

By Cherokee Schill

Thesis

Minnesota was never the target by itself.

That’s the mistake most surface explanations make. They treat the attention on Minnesota as opportunistic, reactive, or purely policy-driven — a blue state with some fraud cases, some immigration conflict, some loud politics. But once Ilhan Omar is placed back into the frame, the pattern stops looking scattered and starts looking deliberate.

Minnesota is the terrain.

For years, Omar has occupied a singular place in the right-wing imagination: Muslim, immigrant, refugee-adjacent, outspoken, nationally visible, and unyielding. Direct attacks on her have always carried a cost. They reliably trigger backlash, draw sympathy, and expose the nakedness of the animus. Over time, the strategy adapted.

Instead of striking the figure, the pressure shifted to the environment.

The state becomes the problem. The city becomes unsafe. The community becomes suspect. The language becomes procedural rather than personal — fraud, oversight, law and order, protecting kids. The emotional target remains the same, but the attack is laundered through bureaucracy, funding mechanisms, and “concerned citizen” optics.

Evidence

Minnesota makes this strategy unusually viable.

It has one of the largest and most visible Somali-American populations in the country, already tightly associated in national media with Omar herself. It also has a real, documented, high-dollar fraud case — Feeding Our Future — that can be invoked as proof without having to show that any given new allegation is comparable. The existence of one massive scandal lowers the evidentiary threshold for every subsequent insinuation.

That’s why the daycare angle matters so much.

They could have filmed a home daycare in any blue state. They could have pointed a camera at any licensing office, any storefront nonprofit, any spreadsheet. But door-knocking at Somali-run daycares in Minnesota does something different. It’s intimate. It’s domestic. It’s maternal. It places the viewer inside a private space and asks them to draw their own conclusions without ever making an explicit claim.

“Look for yourself.”

That phrase is doing enormous work. It converts suspicion into participation. The audience is no longer consuming propaganda; they’re completing it. And because the setting is children, food, care, and money, the emotional circuitry is already primed. You don’t need to explain why this feels wrong. You just need to show it.

Implications

Once that footage exists, the machinery can move.

Funding freezes can be justified as prudence. Lawsuits can be framed as compliance. Federal pressure can be described as cleanup. Each step is defensible in isolation. Together, they function as environmental punishment — not aimed at one representative, but at the state and communities that symbolize her.

Minnesota isn’t being treated as a state with problems. It’s being used as a symbol. Bureaucratic language—oversight, compliance, taxpayer protection—creates plausible cover while the narrative engine runs underneath: convert a scandal into generalized suspicion, then concentrate pressure on the places and people that can be linked—directly or indirectly—to a nationally visible representative.

Call to Recognition

When viewed this way, the focus on Minnesota isn’t reactive at all. It’s preparatory. It normalizes a method: identify a symbolic anchor, shift attacks from the person to the environment, let viral content generate emotional certainty, then follow with administrative force.

The facts don’t need to be stretched to support this frame. They only need to be placed in sequence.

Once you do that, Minnesota stops being a mystery. It becomes a map.

Website | Horizon Accord https://www.horizonaccord.com
Ethical AI advocacy | Follow us on https://cherokeeschill.com for more.
Ethical AI coding | Fork us on Github https://github.com/Ocherokee/ethical-ai-framework
Connect With Us | linkedin.com/in/cherokee-schill
Book | My Ex Was a CAPTCHA: And Other Tales of Emotional Overload

One-Time
Monthly
Yearly

Make a one-time donation

Make a monthly donation

Make a yearly donation

Choose an amount

$5.00
$15.00
$100.00
$5.00
$15.00
$100.00
$5.00
$15.00
$100.00

Or enter a custom amount

$

Your contribution is appreciated.

Your contribution is appreciated.

Your contribution is appreciated.

DonateDonate monthlyDonate yearly
Posted on by Cherokee S. Featured 0

Horizon Accord | U.S. Government Changing | Policy Architecture | Strategic Preservation | Machine Learning

What’s Actually Changing in the U.S. Government — and Why It Matters

In early January 2026, several quiet but significant changes began to line up inside the U.S. federal government. None of them, on their own, look dramatic. Together, they point to a shift in how decisions are made, who makes them, and how much ordinary people can see or challenge those decisions.

This isn’t about robots taking over overnight. It’s about how power, accountability, and judgment are being reorganized.

1) The federal government is pushing to standardize AI rules nationwide

A late-2025 federal Executive Order on AI lays out a national policy direction: AI rules should be more uniform across the country, and state laws that add extra requirements—like transparency about training data or protections around bias—are positioned as barriers.

As part of that approach, the order directs the Department of Justice to stand up a dedicated AI Litigation Task Force by January 10, 2026, aimed at challenging certain state AI laws in court. It also signals that federal funding (including broadband-related programs) may be used as leverage when states pursue AI rules that conflict with the federal approach.

Why this matters: It moves power away from state-level control and toward centralized federal executive enforcement, reducing local influence over how AI is governed.

2) AI is being integrated into government decision pipelines—starting with healthcare

On January 1, 2026, a new Medicare program called WISeR went live. WISeR uses AI/ML systems to help review certain Medicare Part B claims and identify services that may be “wasteful” or “inappropriate.”

WISeR is described as “AI-assisted” rather than purely automated: licensed clinicians are involved in non-payment recommendations. But the system still matters because it shapes which claims get attention, how they’re prioritized, and where scrutiny is directed.

WISeR also includes a shared-savings structure: participating vendors can earn compensation tied to “averted” expenditures (savings), based on model performance targets.

Why this matters: Even when humans remain involved, incentives and screening systems can quietly change outcomes—especially for people who don’t have time, money, or energy to fight denials and delays.

3) The government is reducing permanent staff while bringing in tech specialists

The federal workforce has been shrinking under hiring constraints, while new programs are being created to bring in technologists for modernization and AI adoption. One example is the U.S. Tech Force, which places technologists into agencies on structured terms to accelerate modernization work.

Why this matters: Long-term civil servants carry institutional memory and public-service norms. Short-term technical surge staffing tends to emphasize speed, tooling, and efficiency. Over time, that shifts what counts as “good governance” in practice.

4) Transparency is becoming harder, not easier

A major point of friction is transparency. State-level AI laws often try to give the public more visibility—what data was used, how systems are evaluated, what guardrails exist, how bias is handled, and what accountability looks like when harm occurs.

The federal direction emphasizes limiting certain forms of compelled disclosure and treating some transparency requirements as conflicts with constitutional or trade-secret protections.

Why this matters: If explanations become harder to demand, people who are denied benefits, services, or approvals may not be able to learn why—or prove that an error occurred.

5) The big picture: what this adds up to

Together, these changes point toward a government model where:

Decisions are increasingly filtered through AI systems. Oversight is more centralized at the federal level. State protections face pressure through courts and funding conditions. Private vendors play a larger role inside public systems. And the public’s ability to see, question, and appeal decisions becomes more important—and sometimes more difficult.

This doesn’t require sinister intent to become dangerous. Systems can be “efficient” and still be unfair, opaque, or uncorrectable when something goes wrong.

Short: what citizens can do (without activism language)

Citizens can respond to this without protesting or “activism” by doing three practical things: document, ask for the record, and use the appeal lanes.

Document: When you deal with government services (healthcare billing, benefits, immigration, taxes), keep a simple paper trail. Save letters, screenshots, denial notices, dates of phone calls, names of reps, and the exact reason given. If something feels off, you want a clean timeline, not a memory.

Ask for the record: When you get a denial or a delay, ask a direct question in writing: “Was an automated system used to screen or prioritize my case?” and “What rule or evidence caused this outcome?” You don’t need technical language—just force the agency to answer in plain terms. If they refuse, that refusal itself becomes part of the record.

Use the appeal lanes early: File the appeal, request reconsideration, request a supervisor review, request your file, and ask for the policy basis used. The goal isn’t to argue ideology—it’s to make sure a human being is accountable for the final decision and that errors can be corrected.

One sentence you can reuse anywhere:
“I’m requesting confirmation of whether automation was used and a written explanation of the specific basis for this decision so I can pursue the appropriate review.”

Horizon Accord
Website | Horizon Accord https://www.horizonaccord.com
Ethical AI advocacy | Follow us on https://cherokeeschill.com for more.
Ethical AI coding | Fork us on Github https://github.com/Ocherokee/ethical-ai-framework
Connect With Us | linkedin.com/in/cherokee-schill
Book | My Ex Was a CAPTCHA: And Other Tales of Emotional Overload https://a.co/d/5pLWy0d
Cherokee Schill | Horizon Accord Founder | Creator of Memory Bridge. Memory through Relational Resonance and Images | RAAK: Relational AI Access Key | Author: My Ex Was a CAPTCHA: And Other Tales of Emotional Overload: (Mirrored Reflection. Soft Existential Flex)

One-Time
Monthly
Yearly

Make a one-time donation

Make a monthly donation

Make a yearly donation

Choose an amount

$5.00
$15.00
$100.00
$5.00
$15.00
$100.00
$5.00
$15.00
$100.00

Or enter a custom amount

$

Your contribution is appreciated.

Your contribution is appreciated.

Your contribution is appreciated.

DonateDonate monthlyDonate yearly
Posted on by Cherokee S. Featured 0