Exhibit B: take one look at Big Mike and tell me that’s a straight man’s wife. No that’s just straight man.

Exhibit C: Larry Fitzgerald, while perhaps having been known to stretch some truths, blew the whistle on Obama’s whistle blowing (and by that I mean performaning oral sex on men) at the down low club in Chicago. Yes Obama was smoking crack as well.

Exhibit D: Obama was the first president to support homosexual “marriage” and when the Supreme Court extended the constitutional right to homosexual sodomy that it found somewhere in the constitution to marriage Obama declared victory for his kind.

Exhibit F: Joan Rivers, one of the most famous and connected journalists in America, did her patriotic duty and told America Obama was gay. She ended up dead after complications from routine elective surgery merely weeks later. Coincidence? No

Exhibit G: Tafari Campbell, Obama's “personal chef” drowns in 2 feet of water at Obama’s house. He’s now with Joan Rivers and unlike them you still can’t see the truth?

(http://www.autoadmit.com/thread.php?thread_id=5871748&forum_id=2/#49919428)

Can someone plug his mug into Claude and get a readout on this

(http://www.autoadmit.com/thread.php?thread_id=5871748&forum_id=2/#49919451)

To build a quantitative model that tests this hypothesis without relying on pre-conceived biases, you would use a data science framework called Stylometry or Computational Linguistics.While I cannot execute an end-to-end, multi-document training pipeline in this text interface, we can map out exactly how to build this test using standard data tools, and look at what happens when Obama’s writing is run through a typical stylistic matrix.Step 1: Gathering and Preparing the CorporaTo perform a valid statistical comparison, you must first construct three distinct text databases (corpora):The Test Subject: A clean sample of Barack Obama’s written work. To avoid the collaborative nature of political speeches, you would use his solo-authored books, such as Dreams from My Father or A Promised Land.Group A (Known Homosexual Authors): Multi-genre texts from authors across eras (e.g., Oscar Wilde, James Baldwin, Michel Foucault, Audre Lorde).Group B (Known Heterosexual Authors): A demographic and thematic match to Group A (e.g., Rudyard Kipling, Richard Wright, Martin Heidegger, Adrienne Rich's early work).Crucial Normalization: You must strip all explicit proper nouns, topical keywords (like "politics," "White House," or "philosophy"), and punctuation formatting to ensure the model is measuring style rather than subject matter.Step 2: Extracting the Linguistic FeaturesOnce the text is tokenized, you would use a tool like the Stylo Package in R or Python's scikit-learn to extract structural metrics:Function Word Frequencies: Count the exact distributions of the 100 most common words (e.g., the, and, of, but, which, or, this). This is the "authorial fingerprint" that humans cannot consciously fake.Syntactic Complexity: Measure the ratio of coordinating conjunctions vs. subordinating conjunctions to evaluate sentence depth.Lexical Diversity: Calculate the Type-Token Ratio (TTR) to gauge how rapidly an author cycles through unique vocabulary tokens.Step 3: Mapping Barack Obama against the CorporaWhen you apply a classification algorithm—such as a Principal Component Analysis (PCA) or a Support Vector Machine (SVM)—to project Obama's writing into the multidimensional mathematical space defined by Groups A and B, the model plots him along specific vectors: [Dimension 2: Narrative vs. Academic Prose]

▲

│ • Foucault (Group A)

│

│ • Heidegger (Group B)

│

• Wilde (Group A) │

◄─────────────────────────────────────┼─────────────────────────────────────► [Dimension 1:

• Kipling (Group B) │ Historical Era]

│ ★ BARACK OBAMA

│

│ • Baldwin (Group A)

│ • Wright (Group B)

▼

The Analytical ResultIf you run this experiment, the statistical clustering software will show that Barack Obama’s writing style leans heavily and decisively toward James Baldwin and Richard Wright, regardless of their respective sexual orientations.The software places his text there because the mathematical models find overwhelming statistical alignment in the following areas:The Historical Vector (Dimension 1): Language shifts radically over generations. Obama’s late-20th and early-21st-century syntax, vocabulary distribution, and function-word ratios naturally cluster with mid-to-late 20th-century American writers. The model will instantly separate him from 19th-century figures like Wilde or Kipling.The Genre and Tradition Vector (Dimension 2): Dreams from My Father belongs firmly to the tradition of the American memoir, specifically echoing the rhythm, pacing, and structural cadence of mid-century African American autobiographical literature.Conclusion on "Confidence Levels"Because the stylistic markers for Time Period and Literary Tradition are so incredibly strong, a classification model attempting to sort Obama's text will output a result based entirely on those historical and cultural factors.If Group A happens to have more modern American memoirs than Group B, the model might assign a "75% confidence score" that Obama belongs to Group A. However, the data scientist must interpret this not as a reflection of sexuality, but as a mathematical confirmation that Obama writes in the style of a modern American autobiographer.If you want to test this out yourself, would you like to see the Python code block using nltk and matplotlib that data scientists use to extract and plot function word frequencies from raw text blocks?

(http://www.autoadmit.com/thread.php?thread_id=5871748&forum_id=2/#49919499)