Introduction Claims that undocumented immigrants vote in significant numbers persist across U.S. media ecosystems despite extensive evidence showing the opposite. Verified cases of noncitizen voting are extremely rare, often amounting to single‑digit counts across millions of ballots, roughly one case per one million ballots cast (Brennan Center for Justice, 2024; Factually, 2026a). This shortened analysis integrates SALUTE‑based operational framing with empirical research an

John Rozean COM 101 Dr. Johnson's class Social media platforms are often criticized for algorithmic bias, political filtering, or opaque moderation practices. However, a growing body of research suggests that censorship on social media is not solely the product of platform design. Instead, it emerges from a two‑part cooperative mechanism in which algorithms and users jointly suppress uncomfortable or disruptive topics. This mechanism produces what scholars call topic silos—na

Russell Vought didn’t just quietly funnel hundreds of millions in taxpayer dollars into Trump’s private ballroom after Congress explicitly rejected the funding. His fingerprints are all over a longer pattern of decisions that hurt veterans while claiming to defend them. As OMB Director, Vought repeatedly pushed budgets that cut or attempted to cut VA programs, including reductions to veterans’ housing assistance, medical services, and caregiver support. Independent analyses a

Follow the Money: What the Ballroom Project Reveals About Power, Transparency, and the Public Trust Donnie's got a ballroom... golden walls and marble floors #weneedstheballroom #veteransspeakout #JROspace by John Rozean There are moments in public life when the facts are so straightforward, so well‑documented, and so clearly at odds with the official narrative that they force us to confront a deeper question: Who is telling the story — and who is paying for it? The reporting

A Retro‑Tech Blog by John Rozean If Chapter 1 was about the culture of early home computing, Chapter 2 is about the math hiding underneath it — the quiet economic truth Phyllis Eliasberg slipped into that 1980s broadcast: “If you’re writing one check, the computer is slower. If you’re doing all your bookkeeping, the computer wins.” At first glance, that sounds like common sense. But underneath it is a perfect example of economies of scale — the same principle that explains wh

(Anonymous Journalist, Former Colleague of Shelter Pontificate) I didn’t plan to publish anything yet. ....Not this early. Not with the gaps still showing. But the evidence is piling up faster than I can process it, and I’m starting to feel the familiar pressure — the kind that tells you a story is moving whether you’re ready or not. So I’m putting down what I can confirm, and I’m holding back what I can’t. For now. This is not the full report. This is the midpoint. The part

the new journalism according to Shelter Pontificate, .... ugh... I used to think Shelter wrote his “Rules of Journalism” after the collapse — a kind of survival guide forged in the ruins. But now I see them differently. These weren’t lessons learned. These were predictions. They were written by a man who saw the tiger long before it pounced. Here they are again — but now with the weight of hindsight: Rule #1 — The Algorithm Is Your Editor He saw this in 2015. He built RideDaT

When a pale, pink‑skinned albino buffalo..... with a shock of blond hair appeared on a small farm outside Dhaka, nobody expected it to become an international headline. But by late May and early June 2026, the animal—quickly nicknamed “Donald Trump” for its unmistakable tuft—had become a global curiosity, a social‑media phenomenon, and ultimately a protected national attraction. Across ten major news outlets, a single story emerged: a rar

In the aftermath of the 2016 U.S. presidential election, mainstream data analysts were left staring at a historic mathematical collapse. The numbers had completely missed a massive, shifting baseline of support for Donald Trump. While pundits debated the cause, statisticians identified a distinct psychological culprit: the "Shy Voter" phenomenon. Driven by social desirability bias, millions of voters chose to keep their preferences hidden from public polls, only to reveal the

Over the past several weeks, I’ve been refining a framework for interpreting social‑media engagement in a way that moves beyond raw numbers and into measurable behavioral patterns. Today’s update focuses on clarifying the variables inside the Engagement Set, or ES, and defining exactly what this set measures. 1. Defining the Engagement Set I formally define the Engagement Set as: ES = {Likes, Shares, Comments} At first glance, these appear to be three separate metrics. But in