Revolution

The assertion of English as a pivot of civilization in the 20th and 21st centuries is a thread woven through the ambitions and actions of Rupert Murdoch, Pastor Gary Skinner, Elon Musk, and Donald Trump, each embodying a distinct phase in the journey from noise to signal as outlined in the concept of transvaluation. This process, a trajectory from chaos to clarity, mirrors their efforts to impose order—linguistic, cultural, or ideological—on a disordered world, with English as the fulcrum. At 93, Murdoch frets over his legacy through “Project Harmony,” a late-life endeavor to unify his media empire’s influence under a single vision, while Musk’s obsession with eugenics and Trump’s recent executive order declaring English the official language of the United States reflect their own attempts to sculpt meaning from ambiguity. Meanwhile, Skinner’s English-speaking church in Uganda emerges as a curious footnote, a missionary impulse rooted in divine obedience. Together, they illustrate how the English language has become both tool and symbol in their quests for mastery over the modern era’s noise.

Murdoch’s story begins in the near-chaos of inheritance, a 95/5 noise-to-signal ratio where the raw materials of his father’s modest Australian newspaper empire thrust him into a world of unformed potential. Born in 1931, he took the reins at 21, transforming a single publication into a global media juggernaut—News Corp—that spans The Sun, The Wall Street Journal, and Fox News. His “Project Harmony,” an effort to consolidate control for his eldest son Lachlan, reflects an aging titan’s shift toward the 80/20 stage of pattern recognition, where he seeks to impose a coherent legacy on a sprawling, fractious empire. English, as the lingua franca of his outlets, is no mere vehicle; it’s the signal he’s refined from the noise of competing voices, wielding it to shape political narratives from Brexit to Trump’s rise. At 93, Murdoch’s anxiety over his legacy reveals a man clutching at the 5/95 mastery phase, desperate to ensure his English-language influence endures beyond his mortality, even as family rivalries and a shifting media landscape threaten to unravel it.

Fig. 27 What Exactly is Identity. A node may represent goats (in general) and another sheep (in general). But the identity of any specific animal (not its species) is a network. For this reason we might have a “black sheep”, distinct in certain ways – perhaps more like a goat than other sheep. But that’s all dull stuff. Mistaken identity is often the fuel of comedy, usually when the adversarial is mistaken for the cooperative or even the transactional.

Pastor Gary Skinner, raised in South Africa’s KwaZulu-Natal, represents a different arc—emerging from the 51/49 ambiguity of instinct versus engagement. In 1980s Uganda, amid postcolonial upheaval, he obeyed what he described as God’s voice, founding Watoto Church—an English-speaking Pentecostal congregation in Kampala. This was no random choice: English, the language of colonial legacy and global reach, became his tool to carve signal from the noise of a war-torn nation’s multilingual chaos. Skinner’s church, now a network with thousands of congregants, mirrors the transvaluative leap from raw instinct (a divine call) to conscious engagement (a structured, English-centric ministry). His mission aligns with a broader evangelical trend, using English to assert a civilizational pivot—less about Uganda’s local tongues and more about a universalist Christian identity tied to the Anglosphere’s cultural dominance. For Skinner, English is the signal of salvation, a bridge from spiritual disorder to divine order.

Elon Musk, by contrast, operates in the 20/80 realm of risk, where signal dominates but hubris looms. Born in South Africa and now a titan of American industry, Musk’s fixation on English as a civilizational anchor is subtler, tangled in his eugenic leanings and technological ambitions. His choice of Canadian partners—mirroring his mother Maye’s origins—hints at a curated lineage, a personal project to refine humanity’s genetic and cultural stock, with English as the implicit medium of his envisioned future. Through X, Musk amplifies English-language discourse, often aligning with Trump’s “Make America Great Again” ethos, as seen in his $200 million push to elect Trump in 2024. His Department of Government Efficiency (DOGE) role under Trump further cements this, slashing waste in an English-speaking bureaucracy he aims to perfect. Musk’s English isn’t just a language; it’s the signal of a new order—colonial in scope, Martian in aspiration—where noise is the inefficiency he seeks to eradicate, and eugenics whispers beneath his technocratic veneer.

Donald Trump’s recent executive order on February 28, 2025, making English the official U.S. language, lands him squarely in the 5/95 mastery phase—triumphant yet fragile. Who cares, one might ask, in a nation where English already reigns? Yet this move, signed with Murdoch and Musk in orbit, is less practical than symbolic: a capstone to his MAGA narrative, compressing the noise of multiculturalism into a singular, English-defined signal of American identity. Trump’s alliance with Murdoch’s Fox News and Musk’s X has long relied on English as the battering ram of populist clarity—think “Build the Wall” or “Fake News”—distilling complex realities into blunt, memorable slogans. This order, echoing his father Fred’s real-estate empire of exclusion, asserts English as the pivot of a civilization he claims to defend, even as the 5% noise of dissent (legal challenges, global indifference) lingers. It’s hubris dressed as policy, a final flourish in his chaotic march from reality TV to the White House.

These four figures—Murdoch, Skinner, Musk, and Trump—converge on English as a civilizational linchpin, each navigating the transvaluative arc from noise to signal in their own way. Murdoch’s media empire refined it into a tool of influence, anxious to secure his legacy; Skinner’s church wielded it as a spiritual unifier; Musk’s technocratic vision casts it as the language of a superior future; and Trump’s decree stamps it as the emblem of a reclaimed America. Across the English-speaking world, their efforts reflect a shared belief: that this language, born of empire and spread by power, can compress the chaos of modernity into a signal of order. Yet, as transvaluation warns, the 5% noise persists—a bear in the bush, a reminder that their mastery, like all human triumphs, dances on the edge of indifference from a universe that speaks no tongue at all.

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import numpy as np
import matplotlib.pyplot as plt
import networkx as nx

# Define the neural network layers
def define_layers():
    return {
        'Suis': ['Foundational', 'Grammar', 'Nourish It', 'Know It', "Move It", 'Injure It'],  # Static
        'Voir': ['Gate-Nutrition'],  
        'Choisis': ['Prioritize-Lifestyle', 'Basal Metabolic Rate'],  
        'Deviens': ['Unstructured-Intense', 'Weekly-Calendar', 'Refine-Training'],  
        "M'èléve": ['NexToken Prediction', 'Hydration', 'Fat-Muscle Ratio', 'Visceral-Fat', 'Existential Cadence']  
    }

# Assign colors to nodes
def assign_colors():
    color_map = {
        'yellow': ['Gate-Nutrition'],  
        'paleturquoise': ['Injure It', 'Basal Metabolic Rate', 'Refine-Training', 'Existential Cadence'],  
        'lightgreen': ["Move It", 'Weekly-Calendar', 'Hydration', 'Visceral-Fat', 'Fat-Muscle Ratio'],  
        'lightsalmon': ['Nourish It', 'Know It', 'Prioritize-Lifestyle', 'Unstructured-Intense', 'NexToken Prediction'],
    }
    return {node: color for color, nodes in color_map.items() for node in nodes}

# Define edge weights (hardcoded for editing)
def define_edges():
    return {
        ('Foundational', 'Gate-Nutrition'): '1/99',
        ('Grammar', 'Gate-Nutrition'): '5/95',
        ('Nourish It', 'Gate-Nutrition'): '20/80',
        ('Know It', 'Gate-Nutrition'): '51/49',
        ("Move It", 'Gate-Nutrition'): '80/20',
        ('Injure It', 'Gate-Nutrition'): '95/5',
        ('Gate-Nutrition', 'Prioritize-Lifestyle'): '20/80',
        ('Gate-Nutrition', 'Basal Metabolic Rate'): '80/20',
        ('Prioritize-Lifestyle', 'Unstructured-Intense'): '49/51',
        ('Prioritize-Lifestyle', 'Weekly-Calendar'): '80/20',
        ('Prioritize-Lifestyle', 'Refine-Training'): '95/5',
        ('Basal Metabolic Rate', 'Unstructured-Intense'): '5/95',
        ('Basal Metabolic Rate', 'Weekly-Calendar'): '20/80',
        ('Basal Metabolic Rate', 'Refine-Training'): '51/49',
        ('Unstructured-Intense', 'NexToken Prediction'): '80/20',
        ('Unstructured-Intense', 'Hydration'): '85/15',
        ('Unstructured-Intense', 'Fat-Muscle Ratio'): '90/10',
        ('Unstructured-Intense', 'Visceral-Fat'): '95/5',
        ('Unstructured-Intense', 'Existential Cadence'): '99/1',
        ('Weekly-Calendar', 'NexToken Prediction'): '1/9',
        ('Weekly-Calendar', 'Hydration'): '1/8',
        ('Weekly-Calendar', 'Fat-Muscle Ratio'): '1/7',
        ('Weekly-Calendar', 'Visceral-Fat'): '1/6',
        ('Weekly-Calendar', 'Existential Cadence'): '1/5',
        ('Refine-Training', 'NexToken Prediction'): '1/99',
        ('Refine-Training', 'Hydration'): '5/95',
        ('Refine-Training', 'Fat-Muscle Ratio'): '10/90',
        ('Refine-Training', 'Visceral-Fat'): '15/85',
        ('Refine-Training', 'Existential Cadence'): '20/80'
    }

# Calculate positions for nodes
def calculate_positions(layer, x_offset):
    y_positions = np.linspace(-len(layer) / 2, len(layer) / 2, len(layer))
    return [(x_offset, y) for y in y_positions]

# Create and visualize the neural network graph
def visualize_nn():
    layers = define_layers()
    colors = assign_colors()
    edges = define_edges()
    G = nx.DiGraph()
    pos = {}
    node_colors = []
    
    # Create mapping from original node names to numbered labels
    mapping = {}
    counter = 1
    for layer in layers.values():
        for node in layer:
            mapping[node] = f"{counter}. {node}"
            counter += 1
            
    # Add nodes with new numbered labels and assign positions
    for i, (layer_name, nodes) in enumerate(layers.items()):
        positions = calculate_positions(nodes, x_offset=i * 2)
        for node, position in zip(nodes, positions):
            new_node = mapping[node]
            G.add_node(new_node, layer=layer_name)
            pos[new_node] = position
            node_colors.append(colors.get(node, 'lightgray'))
    
    # Add edges with updated node labels
    for (source, target), weight in edges.items():
        if source in mapping and target in mapping:
            new_source = mapping[source]
            new_target = mapping[target]
            G.add_edge(new_source, new_target, weight=weight)
    
    # Draw the graph
    plt.figure(figsize=(12, 8))
    edges_labels = {(u, v): d["weight"] for u, v, d in G.edges(data=True)}
    
    nx.draw(
        G, pos, with_labels=True, node_color=node_colors, edge_color='gray',
        node_size=3000, font_size=9, connectionstyle="arc3,rad=0.2"
    )
    nx.draw_networkx_edge_labels(G, pos, edge_labels=edges_labels, font_size=8)
    plt.title("OPRAH™: Existential Rupert Cadence", fontsize=25)
    plt.show()

# Run the visualization
visualize_nn()

Fig. 28 Francis Bacon. He famously stated “If a man will begin with certainties, he shall end in doubts; but if he will be content to begin with doubts, he shall end in certainties.” This quote is from The Advancement of Learning (1605), where Bacon lays out his vision for empirical science and inductive reasoning. He argues that starting with unquestioned assumptions leads to instability and confusion, whereas a methodical approach that embraces doubt and inquiry leads to true knowledge. This aligns with his broader Novum Organum (1620), where he develops the Baconian method, advocating for systematic observation, experimentation, and the gradual accumulation of knowledge rather than relying on dogma or preconceived notions.