Uncertainty, 🌊

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Analysis

In designing the scenery and costumes for any of Shakespeare’s plays, the first thing the artist has to settle is the best date for the drama. This should be determined by the general spirit of the play, more than by any actual historical references which may occur in it. Most Hamlets I have seen were placed far too early. Hamlet is essentially a scholar of the Revival of Learning; and if the allusion to the recent invasion of England by the Danes puts it back to the ninth century, the use of foils brings it down much later. Once, however, that the date has been fixed, then the archæologist is to supply us with the facts which the artist is to convert into effects.

-- The Truth of Masks 🎭

Athena’s Filter, Dionysus’ Cry, and Apollo’s Mask: An Epistemic Dissection of Scientific Assault in Trump-Era Politics

Fig. 1 Yes trust them not: for there is an upstart Crow, beautified with our feathers. Source: Robert Greene

The confrontation between state power and scientific independence is neither new nor uniquely American, but in the context of the Trump administration’s systematic undermining of research institutions, we must examine the clash through a mythopoetic lens—one framed not by neutrality, but by hunger, fury, and the aching need for beauty. If Dionysus symbolizes the unfiltered, anarchic truth—the screaming data, the toxic spill, the aerosolized virus—and Apollo is the patron of symmetry, lyricism, and comfort, then Athena is the necessary intermediary. Her helm does not merely protect; it refracts. Her spear is not just a weapon—it is an instrument of precision. In this trinity, science is neither Dionysian chaos nor Apollonian illusion. It is the Athenian filter applied to reality, disciplined into coherence without surrendering to delusion.

And yet the Trump-era political ethos rejected Athena altogether. It plunged into a grotesque Apollonian fantasy—a propagandistic dream world where truth is only tolerated if it flatters. The administration’s evisceration of public datasets, firing of federal scientists, and cancellation of training programs was not just a budgetary choice; it was the scorched-earth retreat from Athena’s guardianship. This was not a fight over facts. This was a war against the very faculty of discernment—against the owl’s nocturnal gaze, the serpent’s coiled wisdom, the capacity to see into the murk and emerge with something approximating actionable clarity.

Fig. 2 What is being optimized? Capital gains or human welfare? It’s convenient to say that optimizing capital gains is a complex process, wherein improved human welfare is an emergent, unintended phenomenon

Science, in its truest form, is not neutral. It is ravenous. It wants to know. It trespasses. It is Dionysian in origin, seeking to touch what is veiled. But without Athena, science remains raw, dangerous, and incomprehensible to the polis. The purpose of the Athenian filter is precisely to transmute such dangerous truths into meaningful policy—something that neither silences Dionysus nor sedates him with Apollo’s lullaby. And yet, what we saw under Trump was the exile of Athena, a triumph of spectacle over discernment, of charismatic certainty over iterative method.

The open letter by the National Academies’ scientists was not merely an act of protest; it was a desperate invocation of Athena. Their collective plea—“we are sending this SOS”—is a ritual cry, a Homeric chorus summoning the goddess back into the agora. These are not bureaucrats lamenting job cuts. These are elders of the scientific temple warning that the sacred tools—peer review, reproducibility, open data—are being desecrated. And the stakes are not abstract. This is about the health of children, the safety of water, the resilience of forests, the survival of truth itself.

In our symbolic cosmology—🌊 for unfiltered truth, 🚢 for inherited structure, 🪛🏴‍☠️ for strategic resistance, ✂️🦈🛟 for discernment, risk, and grace, and 🏝️ for ideology or final meaning—we see that science occupies the precarious position of the raft. It is not the island, despite what technocrats claim. Nor is it the ship of myth handed down. It is the raft cobbled together from data, theory, instrumentation, and debate—always provisional, always vulnerable, always one shark bite away from oblivion. But it floats. And it saves lives.

Trump’s dismantling of science institutions was thus not simply an anti-intellectual maneuver. It was a symbolic rupture in the epistemic architecture of the state. By removing the Athena-filter—by muzzling climate scientists, firing CDC officials, and undermining the FDA—the administration chose to navigate the stormy sea without map, compass, or raft. It plunged the nation into Dionysian chaos while insisting on an Apollonian delusion. And the citizens, caught in the middle, found themselves both drowning and dreaming.

Fig. 3 Consider pretext, subtext, text, context, metatext. It is text in the mode of holy-writ that makes faustian bargains vs. islamic finance the ultimate bifurcation in how systems are engineered.

The owl, in our mythic language, symbolizes silent insight, the kind that sees through darkness. The Trump administration preferred the peacock. It offered spectacle, not wisdom. It recoiled from the serpent’s uncomfortable truths—of systemic racism, ecological fragility, pandemic mismanagement—and instead wrapped itself in the aegis of nationalism and economic bravado. But what good is a shield that blinds instead of reveals? What virtue in a helmet that muffles rather than protects?

The scientists’ letter was a momentary reinstatement of the Athenian imperative. Not an overthrow, not a revolution, but a recalibration. A reminder that the point of science is not to please power, but to inform it. And that without Athena, neither Apollo nor Dionysus can guide a polis—only ruin it.

We must also acknowledge that the Trumpian epistemology was not purely novel. It drew on deep American tendencies toward anti-intellectualism, mistrust of elites, and the seductive call of rugged individualism over collective insight. These instincts, while mythologically potent, are epistemically suicidal. The pirate flag and screwdriver—🏴‍☠️🪛—symbols we’ve used to represent strategic rebellion—must be distinguished from brute sabotage. The former challenges the ship to improve. The latter sets it ablaze.

In that light, the scientific community must also reckon with its own role. Where was Athena before the crisis? Had she grown haughty? Had the academy’s own illusions become too Apollonian—too self-congratulatory, too detached from the anxieties of the common person? Perhaps. Perhaps Trumpism did not invent the fire but merely ignited a pile of dry credibility.

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
def define_layers():
    return {
        'Tragedy (Pattern Recognition)': ['Cosmology', 'Geology', 'Biology', 'Ecology', "Symbiotology", 'Teleology'],
        'History (Resources)': ['Resources'],  
        'Epic (Negotiated Identity)': ['Faustian Bargain', 'Islamic Finance'],  
        'Drama (Self vs. Non-Self)': ['Darabah', 'Sharakah', 'Takaful'],  
        "Comedy (Resolution)": ['Cacophony', 'Outside', 'Ukhuwah', 'Inside', 'Symphony']  
    }

# Assign colors to nodes
def assign_colors():
    color_map = {
        'yellow': ['Resources'],  
        'paleturquoise': ['Teleology', 'Islamic Finance', 'Takaful', 'Symphony'],  
        'lightgreen': ["Symbiotology", 'Sharakah', 'Outside', 'Inside', 'Ukhuwah'],  
        'lightsalmon': ['Biology', 'Ecology', 'Faustian Bargain', 'Darabah', 'Cacophony'],
    }
    return {node: color for color, nodes in color_map.items() for node in nodes}

# Define edges
def define_edges():
    return [
        ('Cosmology', 'Resources'),
        ('Geology', 'Resources'),
        ('Biology', 'Resources'),
        ('Ecology', 'Resources'),
        ("Symbiotology", 'Resources'),
        ('Teleology', 'Resources'),
        ('Resources', 'Faustian Bargain'),
        ('Resources', 'Islamic Finance'),
        ('Faustian Bargain', 'Darabah'),
        ('Faustian Bargain', 'Sharakah'),
        ('Faustian Bargain', 'Takaful'),
        ('Islamic Finance', 'Darabah'),
        ('Islamic Finance', 'Sharakah'),
        ('Islamic Finance', 'Takaful'),
        ('Darabah', 'Cacophony'),
        ('Darabah', 'Outside'),
        ('Darabah', 'Ukhuwah'),
        ('Darabah', 'Inside'),
        ('Darabah', 'Symphony'),
        ('Sharakah', 'Cacophony'),
        ('Sharakah', 'Outside'),
        ('Sharakah', 'Ukhuwah'),
        ('Sharakah', 'Inside'),
        ('Sharakah', 'Symphony'),
        ('Takaful', 'Cacophony'),
        ('Takaful', 'Outside'),
        ('Takaful', 'Ukhuwah'),
        ('Takaful', 'Inside'),
        ('Takaful', 'Symphony')
    ]

# 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()

    G = nx.DiGraph()
    pos = {}
    node_colors = []

    # Numbered node labels
    mapping = {}
    counter = 1
    for layer in layers.values():
        for node in layer:
            mapping[node] = f"{counter}. {node}"
            counter += 1

    # Add nodes and 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
    edge_colors = {}
    for source, target in edges:
        if source in mapping and target in mapping:
            new_source = mapping[source]
            new_target = mapping[target]
            G.add_edge(new_source, new_target)
            edge_colors[(new_source, new_target)] = 'lightgrey'

    # Add black highlight edges
    numbered_nodes = list(mapping.values())
    black_edge_list = [
        (numbered_nodes[3], numbered_nodes[6]),   # 4 -> 7
        (numbered_nodes[6], numbered_nodes[8]),   # 7 -> 9
        (numbered_nodes[8], numbered_nodes[10]),  # 9 -> 11
        (numbered_nodes[10], numbered_nodes[12]), # 11 -> 13
        (numbered_nodes[10], numbered_nodes[13]),
        (numbered_nodes[10], numbered_nodes[14]),
        (numbered_nodes[10], numbered_nodes[15]),
        (numbered_nodes[10], numbered_nodes[16])
    ]
    for src, tgt in black_edge_list:
        G.add_edge(src, tgt)
        edge_colors[(src, tgt)] = 'black'

    # Draw the network
    plt.figure(figsize=(12, 8))
    nx.draw(
        G, pos, with_labels=True, node_color=node_colors, 
        edge_color=[edge_colors.get(edge, 'lightgrey') for edge in G.edges],
        node_size=3000, font_size=9, connectionstyle="arc3,rad=0.2"
    )
    plt.title("CG-BEST", fontsize=18)

    # ✅ Save the actual image *after* drawing it
    plt.savefig("figures/cgbest.jpeg", dpi=300, bbox_inches='tight')
    # plt.show()

# Run it
visualize_nn()

Fig. 4 🪡🔥🛠️🏝️ The CG-BEST model rendered as a neural network. A hierarchy of tragedy, history, epic, drama, and comedy—reflected as colored paths of descent and connection. Black edges signal the spine of the epistemic crucible.

But it is also true that when the flames came, it was the scientists who ran toward the raft. They patched the holes. They called out into the storm. They remembered their training. They remembered Athena. And they chose, despite everything, to speak.

This moment must be remembered not just as a political scandal but as an epistemological tragedy. A moment when the compass was flung overboard and the sea—the great 🌊—was mistaken for a playground rather than the abyss. And it is only through Athena, not Apollo, that we regain navigation.

So let us elevate this narrative into our symbolic frame: The Trump administration was a rogue tide, a Dionysian surge weaponized and clad in Apollonian deceit. The scientists were the cingulo-insular function—the salience network activated by threat. The raft was science under siege, patched by Athena’s weary hands. And the island—the imagined safety of knowledge used wisely—remains distant, flickering, not yet reached.

But the spear still gleams. The owl still flies. The serpent still waits beneath the shield. And Athena—if summoned by enough voices—may yet return.

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LoR

Writing Letters of Recommendation: Best Practices

Office of Medical Student Affairs | Johns Hopkins University School of Medicine

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Thank you for taking the @me to support our medical students as they apply for residency. As you know, leEers of recommenda@on (LoRs) play an important role in interview offer decisions. We have put together this document to offer guidance in composing LoRs for our future physician colleagues.

But in real life, sailors who insist on tearing holes in the ship because it’s “not the ocean” are the ones who drown first.
— Yours Truly & GPT-4o

The goal of the LoR is to provide an overall assessment of the candidate’s poten@al to excel as a resident physician. It may help to meet with the student to learn about their career goals and leadership, research, and/or service ac@vi@es. Do not ask students to dra: residency LoRs. Set aside at least an hour to compose a LOR. Importantly, if you do not feel you can write the student a strong LoR, tell the student, so they can find another leEer writer if they choose.

The most helpful leEers contain the following:

• How long and in what capacity you have known the student, and if they have waived their right to see the leEer;

• Your assessment of the student’s abili@es; and

• A summary suppor@ng the strength of your recommenda@on.

Share specific details about the student’s performance, generally focusing on your observa@ons of them in the clinical arena. Topics to consider include:

• Fund of knowledge

• Medical decision making

• Specialty-specific informa@on (OR skills, proficiency at counseling, etc.)

• Technical abili@es

• Communica@on

• Teamwork

• Leadership characteris@cs or experiences

• Passion for medicine or specialty

• Comments from team members, pa@ents or families

• Professionalism

• Ways the student exceeded expecta@ons

• Outstanding professional traits, such as work ethic

In describing any weaknesses, consider whether you can frame them posi@vely (e.g., “demonstrated improvement in documenta@on” rather than “had weak documenta@on”). Do not menton age, race/ethnicity, marital status, children, physical characteristics or other personal attiributes.

As you write, be aware of paEerns of bias that have been found in evalua@ons and leEers across disciplines:

• Check the length of your leEer: LeEers of recommenda@on are oXen shorter for female than male applicants.

• Emphasize accomplishments, not effort or personality: LeEers for individuals underrepresented in medicine (URM) can overemphasize “grindstone” adjec@ves that describe effort alone, e.g. “hard-working” that associates with effort, but not ability. Similarly, female applicants are more likely to be described as “lovely”, or “caring”, “compassionate”, and “empathic” or “empathe@c”. Be sure to include assessments of skill and knowledge along with posi@ve assessments of work ethic and personality.

• Apply superla@ves based on applicant skills and knowledge, not other characteris@cs: female and URM candidates are more likely to be described as “competent”, while White applicants are more likely to be described using “standout” or “ability” keywords (including “excep@onal”, “best”, and “outstanding”).

Adjec@ves to consider including: successful, excellent, accomplished, outstanding, skilled, knowledgeable, insighaul, resourceful, confident, independent, intellectual.

Balance the following adjec@ves with skill and knowledge-based assessments: caring, compassionate, hard-working, conscien@ous, dependable, diligent, dedicated, tacaul, interpersonal, warm, helpful.

You may wish to use this tool that checks LoRs for evidence of gender bias:
https://www.tomforth.co.uk/genderbias/

If you would like examples of strong leEers of recommenda@on, please reach out to our office:
somstudentaffairs@jhmi.edu

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References

1. Hartman ND et al. A Narra@ve Review of the Evidence Suppor@ng Factors Used by Residency Program Directors to Select Applicants for Interviews. J Grad Med Educ 2019 Jun; 11(3): 268-273.

2. Trix F and Psenka C. Exploring the color of glass: leEers of recommenda@on for female and male medical faculty. Discourse and Society. 2003 March; 14(2): 191–220.

3. Rojek A et al. Differences in Narra@ve Language in Evalua@ons of Medical Students by Gender and Under-represented Minority Status. J Gen Int Med. 2019 April; 34(5): 684–691.