Normative

Trump Wanted an Apology. He Got Maine’s No-Nonsense Governor Instead.

White House warns this ‘will not end well’ for Democratic Gov. Janet Mills in escalating fight over federal education funding
By Zusha Elinson
March 30, 2025, 9:00 am EDT

She is no peakock purposed for pageantry.

President Trump wants an apology from Maine Gov. Janet Mills for her prickly refusal to go along with his executive order banning trans athletes from women’s sports.

But Mills, a flinty former prosecutor who stands just over 5 feet tall, won’t give it to him even as his administration launches several probes into Maine and threatens to pull federal funding.

The Democratic governor is a contrast to a number of powerful chiefs of industry, law and academia who have yielded to Trump under the prospect of losing business or federal dollars. Her stubbornness in the weekslong standoff over the state allowing transgender athletes to compete based on their gender identity hasn’t surprised Mainers.

I'd advise you to consider your position carefully (layer 3 fork in the road), perhaps adopting a more flexible posture (layer 4 dynamic capabilities realized), while keeping your ear to the ground (layer 2 yellow node), covering your retreat (layer 5 Athena's shield, helmet, and horse), and watching your rear (layer 1 ecosystem and perspective).

“She’s got a reputation, and I think it’s well-earned, as a tough cookie,” said Mark Brewer, chair of the University of Maine’s political science department. “She’s not going to back down from someone, especially if she thinks she’s in the right.”

The confrontation between the 77-year-old Mills and the 78-year-old Trump figures to be a test case. How far will the administration go to advance its policy agenda? How far will the governor go if it hits the state’s finances?

“Choosing the rights of men who want to dominate women’s sports over the rights of vulnerable women and girls while blatantly ignoring federal law will not end well for the governor, and the people of Maine deserve better,” said Harrison Fields, a White House spokesman.

In Maine, a Democratic-leaning state with a swath of independent voters, the governor’s stance is being met with cheers from Democrats, disapproval from Republicans—and growing fear from all sides about the possibility of losing education funding from the federal government.

“It’s incredibly shortsighted—we need that money,” said Laurel Libby, a Republican state lawmaker who has tangled with Mills over the transgender issue.

Mills cut her teeth as the state’s first female attorney general, clashing regularly with then-Gov. Paul LePage, a bellicose Republican businessman who has called himself “Trump before Trump.”

“LePage probably gave her a graduate degree in dealing with difficult people,” said Tom Saviello, a former Republican state senator.

Running as a centrist Democrat, Mills easily took the governor’s race in 2018 with more than half of the vote, and won again in 2022, fending off her old rival, LePage, by an even larger margin.

The governor’s feud with Trump sparked in February after Libby, the Republican lawmaker, posted pictures of a transgender student who won a girls’ state pole-vaulting championship and chastised Maine schools for not following Trump’s executive order.

Days later, Trump singled out Mills over the issue in public at a governors meeting at the White House.

Show code cell source Hide code cell source

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

# Define the neural network layers with new culinary-cultural labels
def define_layers():
    return {
        'Suis': ['Fire & Fermentation', 'Salt & Smoke', 'Bitterness & Astringency', 'Umami & Decay', "Structural Complexity", 'Regional Staples'],
        'Voir': ['Rituals of Taste'],  
        'Choisis': ['Acquired Taste', 'Social Shared Preferences'],  
        'Deviens': ['Inflammatory Foods', 'Alcohol & Sedatives', 'Comfort Foods & Rituals'],  
        "M'élève": ['Medicinal Balances', 'Ceremonial Consumption', 'Extreme Flavors', 'Everyday Consumables', 'Cultural Transmission']  
    }

# Assign colors to nodes
def assign_colors():
    color_map = {
        'yellow': ['Rituals of Taste'],  
        'paleturquoise': ['Regional Staples', 'Social Shared Preferences', 'Comfort Foods & Rituals', 'Cultural Transmission'],  
        'lightgreen': ["Structural Complexity", 'Alcohol & Sedatives', 'Ceremonial Consumption', 'Everyday Consumables', 'Extreme Flavors'],  
        'lightsalmon': ['Bitterness & Astringency', 'Umami & Decay', 'Acquired Taste', 'Inflammatory Foods', 'Medicinal Balances'],
    }
    return {node: color for color, nodes in color_map.items() for node in nodes}

# Define edge weights
def define_edges():
    return {
        ('Fire & Fermentation', 'Rituals of Taste'): '1/99',
        ('Salt & Smoke', 'Rituals of Taste'): '5/95',
        ('Bitterness & Astringency', 'Rituals of Taste'): '20/80',
        ('Umami & Decay', 'Rituals of Taste'): '51/49',
        ("Structural Complexity", 'Rituals of Taste'): '80/20',
        ('Regional Staples', 'Rituals of Taste'): '95/5',
        ('Rituals of Taste', 'Acquired Taste'): '20/80',
        ('Rituals of Taste', 'Social Shared Preferences'): '80/20',
        ('Acquired Taste', 'Inflammatory Foods'): '49/51',
        ('Acquired Taste', 'Alcohol & Sedatives'): '80/20',
        ('Acquired Taste', 'Comfort Foods & Rituals'): '95/5',
        ('Social Shared Preferences', 'Inflammatory Foods'): '5/95',
        ('Social Shared Preferences', 'Alcohol & Sedatives'): '20/80',
        ('Social Shared Preferences', 'Comfort Foods & Rituals'): '51/49',
        ('Inflammatory Foods', 'Medicinal Balances'): '80/20',
        ('Inflammatory Foods', 'Ceremonial Consumption'): '85/15',
        ('Inflammatory Foods', 'Extreme Flavors'): '90/10',
        ('Inflammatory Foods', 'Everyday Consumables'): '95/5',
        ('Inflammatory Foods', 'Cultural Transmission'): '99/1',
        ('Alcohol & Sedatives', 'Medicinal Balances'): '1/9',
        ('Alcohol & Sedatives', 'Ceremonial Consumption'): '1/8',
        ('Alcohol & Sedatives', 'Extreme Flavors'): '1/7',
        ('Alcohol & Sedatives', 'Everyday Consumables'): '1/6',
        ('Alcohol & Sedatives', 'Cultural Transmission'): '1/5',
        ('Comfort Foods & Rituals', 'Medicinal Balances'): '1/99',
        ('Comfort Foods & Rituals', 'Ceremonial Consumption'): '5/95',
        ('Comfort Foods & Rituals', 'Extreme Flavors'): '10/90',
        ('Comfort Foods & Rituals', 'Everyday Consumables'): '15/85',
        ('Comfort Foods & Rituals', 'Cultural Transmission'): '20/80'
    }

# Define edges to be highlighted in black
def define_black_edges():
    return {
        ('Fire & Fermentation', 'Rituals of Taste'): '1/99',
        ('Salt & Smoke', 'Rituals of Taste'): '5/95',
    }

# Calculate node positions
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()
    black_edges = define_black_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
    edge_colors = []
    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)
            edge_colors.append('black' if (source, target) in black_edges else 'lightgrey')
    
    # 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=edge_colors,
        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™: Cultural-Culinary Equivalents", fontsize=18)
    plt.show()

# Run the visualization
visualize_nn()

Fig. 22 Space is Apollonian and Time Dionysian. They are the static representation and the dynamic emergent. Ain’t that somethin?

“You’d better comply because otherwise, you’re not getting any federal funding,” Trump warned. “We’ll see you in court,” Mills replied.

The Trump administration brought down the hammer. Agriculture Department funding was paused, and then quickly reinstated, at the University of Maine, temporarily jeopardizing efforts such as blueberry research and 4-H programs.

Federal officials started several investigations and found Maine in violation of a law that prohibits sex-based discrimination in education programs that benefit from federal money.

Mills vowed that any attempts to “unilaterally deprive Maine school children of the benefit of federal funding” would result in the state taking legal action.

Trump tightened the screws in a 7:39 a.m. post on Saturday, March 22, implying that the probes could be settled if Mills said she was sorry.

“We need a full throated apology from the Governor herself, and a statement that she will never make such an unlawful challenge to the Federal Government again, before this case can be settled,” Trump wrote.

On Monday, Mills declined to do so: “My issue is about the rule of law, pure and simple,” Mills told local reporters. “It’s not about transgender sports; it’s about who makes the laws and who enforces the laws. I read the Constitution. The Constitution says that the president, the chief executive, shall take care that the laws be faithfully executed. It doesn’t allow him to make laws out of whole cloth by tweet or Instagram post or press release or executive order.”

On Thursday, Maine school officials said they wouldn’t come to an agreement with the Trump administration to ban trans athletes from women’s sports, citing a state law that protects their participation.

On Friday, the federal government launched a separate probe into Maine’s education department over allegations that school officials aren’t informing parents when their children switch gender identities at school.

The row between Trump and Mills shows no signs of fading, and observers believe it will only be decided in court.

Saviello, the former Republican lawmaker, is certain the beef between two strong-willed individuals won’t be settled in the public arena. Instead, he suggested, it should be settled in the proper Maine way.

“They should go fly-fishing and see who catches the biggest fish or go deer hunting and see who shoots the biggest deer,” he said.