Dear Author. [REDACTED] does not accept the submission of personal works produced by students, independent researchers, or professionals who have not yet attained a doctoral level. This is a moderation policy intended to ensure that publications deposited on the platform originate from qualified researchers affiliated with a recognized institution (REDACTED) and acknowledged for their expertise or previous work in the relevant field of research. This rule applies regardless of the quality or scientific value of the work, which is by no means in question here. We therefore regret to inform you that we are unable to accept this submission. If you wish, we invite you to share your work through other open platforms such as Zenodo, which allow all authors to make their research visible. Thank you for your understanding. Kind regards
Allow me to rephrase this:
Dear Author,
We regret to inform you that whilst your work is not in question, you are. Our platform does not accept submissions from students, independent researchers, or professionals who have not yet acquired the correct ceremonial headgear. This policy exists to ensure that ideas originate from bodies already sanctified by a recognised institution. The content may be rigorous, original, and valuable, but that is neither here nor there. Knowledge, like wine, must age in the right cellar.
Please consider sharing your work elsewhere. Zenodo is very accommodating to the uncredentialled.
Kind regards.
Disappointing, though hardly surprising. This is the same logic as age-based thresholds I have recently taken a hammer to: crude proxies elevated into moral and epistemic gatekeepers. Not ‘is this good?’, but ‘are you old enough, stamped enough, letterheaded enough to be taken seriously?’. A bureaucratic horoscope.
Yes, I use Zenodo. I use PhilPapers. I will continue to do so. But let’s not pretend all platforms are socially equivalent. Journals still function as credibility engines, not because they magically improve truth, but because they distribute legitimacy. To be excluded on status grounds alone is not a quality filter. It is a caste system with footnotes.
And journals already make participation unnecessarily hostile. Many refuse work that has been publicly shared at all, even in preprint form. Lead times stretch to a year or more. The result is that anyone attempting to contribute to live debates is instructed to sit quietly whilst the conversation moves on without them. In a so-called knowledge economy, this is an astonishing self-own.
What we have, then, is a system that:
equates institutional affiliation with epistemic competence,
penalises open dissemination,
and delays circulation until relevance decays.
All in the name of rigour.
I will keep submitting elsewhere. There are other journals. There always are. But let’s stop pretending this is about protecting standards. It is about preserving a hierarchy that mistakes accreditation for insight and treats independent thought as a contamination risk.
Knowledge does not become true by passing through the right doorway. It merely becomes approved. I’ll not witter on about the bollocks of peer review.
Now that A Language Insufficiency Hypothesis has been put to bed — not euthanised, just sedated — I can turn to the more interesting work: instantiating it. This is where LIH stops being a complaint about words and starts becoming a problem for systems that pretend words are stable enough to carry moral weight.
What follows is not a completed theory, nor a universal schema. It’s a thinking tool. A talking point. A diagram designed to make certain assumptions visible that are usually smuggled in unnoticed, waved through on the strength of confidence and tradition.
The purpose of this diagram is not to redefine justice, rescue it, or replace it with something kinder. It is to show how justice is produced. Specifically, how retributive justice emerges from a layered assessment process that quietly asserts ontologies, filters encounters, applies normative frames, and then closes uncertainty with confidence.
Audio: NotebookLM summary podcast of this topic.
Most people are willing to accept, in the abstract, that justice is “constructed”. That concession is easy. What is less comfortable is seeing how it is constructed — how many presuppositions must already be in place before anything recognisable as justice can appear, and how many of those presuppositions are imposed rather than argued for.
The diagram foregrounds power, not as a conspiracy or an optional contaminant, but as an ambient condition. Power determines which ontologies are admissible, which forms of agency count, which selves persist over time, which harms are legible, and which comparisons are allowed. It decides which metaphysical configurations are treated as reasonable, and which are dismissed as incoherent before the discussion even begins.
Justice, in this framing, is not discovered. It is not unearthed like a moral fossil. It is assembled. And it is assembled late in the process, after ontology has been assumed, evaluation has been performed, and uncertainty has been forcibly closed.
This does not mean justice is fake. It means it is fragile. Far more fragile than its rhetoric suggests. And once you see that fragility — once you see how much is doing quiet, exogenous work — it becomes harder to pretend that disagreements about justice are merely disagreements about facts, evidence, or bad actors. More often, they are disagreements about what kind of world must already be true for justice to function at all.
I walk through the structure and logic of the model below. The diagram is also available as a PDF, because if you’re going to stare at machinery, you might as well be able to zoom in on the gears.
Why Retributive Justice (and not the rest of the zoo)
Before doing anything else, we need to narrow the target.
“Justice” is an infamously polysemous term. Retributive, restorative, distributive, procedural, transformative, poetic, cosmic. Pick your flavour. Philosophy departments have been dining out on this buffet for centuries, and nothing useful has come of letting all of them talk at once.
This is precisely where LIH draws a line.
The Language Insufficiency Hypothesis is not interested in pedestrian polysemy — cases where a word has multiple, well-understood meanings that can be disambiguated with minimal friction. That kind of ambiguity is boring. It’s linguistic weather.
What LIH is interested in are terms that appear singular while smuggling incompatible structures. Words that function as load-bearing beams across systems, while quietly changing shape depending on who is speaking and which assumptions are already in play.
“Justice” is one of those words. But it is not usefully analysable in the abstract.
So we pick a single instantiation: Retributive Justice.
Why?
Because retributive justice is the most ontologically demanding and the most culturally entrenched. It requires:
a persistent self
a coherent agent
genuine choice
intelligible intent
attributable causation
commensurable harm
proportional response
In short, it requires everything to line up.
If justice is going to break anywhere, it will break here.
Retributive justice is therefore not privileged in this model. It is used as a stress test.
The Big Picture: Justice as an Engine, Not a Discovery
The central claim of the model is simple, and predictably unpopular:
Justice is not discovered. It is produced.
Not invented in a vacuum, not hallucinated, not arbitrary — but assembled through a process that takes inputs, applies constraints, and outputs conclusions with an air of inevitability.
The diagram frames retributive justice as an assessment engine.
An engine has:
inputs
internal mechanisms
thresholds
failure modes
and outputs
It does not have access to metaphysical truth. It has access to what it has been designed to process.
The justice engine takes an encounter — typically an action involving alleged harm — and produces two outputs:
Desert (what is deserved),
Responsibility (to whom it is assigned).
Everything else in the diagram exists to make those outputs possible.
The Three Functional Layers
The model is organised into three layers. These are not chronological stages, but logical dependencies. Each layer must already be functioning for the next to make sense.
1. The Constitutive Layer
(What kind of thing a person must already be)
This layer answers questions that are almost never asked explicitly, because asking them destabilises the entire process.
What counts as a person?
What kind of self persists over time?
What qualifies as an agent?
What does it mean to have agency?
What is a choice?
What is intent?
Crucially, these are not empirical discoveries made during assessment. They are asserted ontologies.
The system assumes a particular configuration of selfhood, agency, and intent as a prerequisite for proceeding at all. Alternatives — episodic selves, radically distributed agency, non-volitional action — are not debated. They are excluded.
This is the first “happy path”.
If you do not fit the assumed ontology, you do not get justice. You get sidelined into mitigation, exception, pathology, or incoherence.
2. The Encounter Layer
(What is taken to have happened)
This layer processes the event itself:
an action
resulting harm
causal contribution
temporal framing
contextual conditions
motive (selectively)
This is where the rhetoric of “facts” tends to dominate. But the encounter is never raw. It is already shaped by what the system is capable of seeing.
Causation here is not metaphysical causation. It is legible causation. Harm is not suffering. It is recognisable harm. Context is not total circumstance. It is admissible context.
Commensurability acts as a gatekeeper between encounter and evaluation: harms must be made comparable before they can be judged. Anything that resists comparison quietly drops out of the pipeline.
3. The Evaluative Layer
(How judgment is performed)
Only once ontology is assumed and the encounter has been rendered legible does evaluation begin:
proportionality
accountability
normative ethics
fairness (claimed)
reasonableness
bias (usually acknowledged last, if at all)
This layer presents itself as the moral heart of justice. In practice, it is the final formatting pass.
Fairness is not discovered here. It is declared. Reasonableness does not clarify disputes. It narrows the range of acceptable disagreement. Bias is not eliminated. It is managed.
At the end of this process, uncertainty is closed.
That closure is the moment justice appears.
Why Disagreement Fails Before It Starts
At this point, dissent looks irrational.
The system has:
assumed an ontology
performed an evaluation
stabilised the narrative through rhetoric
and produced outputs with institutional authority
To object now is not to disagree about evidence. It is to challenge the ontology that made assessment possible in the first place.
And that is why so many justice debates feel irresolvable.
They are not disagreements within the system. They are disagreements about which system is being run.
LIH explains why language fails here. The same words — justice, fairness, responsibility, intent — are being used across incompatible ontological commitments. The vocabulary overlaps; the worlds do not.
The engine runs smoothly. It just doesn’t run the same engine for everyone.
Where This Is Going
With the structure in place, we can now do the slower work:
unpacking individual components
tracing where ontological choices are asserted rather than argued
showing how “reasonableness” and “fairness” operate as constraint mechanisms
and explaining why remediation almost always requires a metaphysical switch, not better rhetoric
Justice is not broken. It is doing exactly what it was built to do.
That should worry us more than if it were merely malfunctioning.
This essay is already long, so I’m going to stop here.
Not because the interesting parts are finished, but because this is the point at which the analysis stops being descriptive and starts becoming destabilising.
The diagram you’ve just walked through carries a set of suppressed footnotes. They don’t sit at the margins because they’re trivial; they sit there because they are structurally prior. Each one represents an ontological assertion the system quietly requires in order to function at all.
By my count, the model imposes at least five such ontologies. They are not argued for inside the system. They are assumed. They arrive pre-installed, largely because they are indoctrinated, acculturated, and reinforced long before anyone encounters a courtroom, a jury, or a moral dilemma.
Once those ontologies are fixed, the rest of the machinery behaves exactly as designed. Disagreement downstream is permitted; disagreement upstream is not.
In a follow-up essay, I’ll unpack those footnotes one by one: where the forks are, which branch the system selects, and why the alternatives—while often coherent—are rendered unintelligible, irresponsible, or simply “unreasonable” once the engine is in motion.
That’s where justice stops looking inevitable and starts looking parochial.
And that’s also where persuasion quietly gives up.
Written by Bry Willis and ChatGPT 5.2 after a couple of days of back and forth
Humans can’t seem to stop clawing after morality. The primates among us chuck cucumbers when their neighbours get grapes, and the rest of us grumble about fairness on social media. The impulse is practically universal, an evolutionary quirk that kept us from throttling each other long enough to raise children and build cities.
Image: A seemingly perturbed capuchin monkey.
But universality is not objectivity. Just because every ape howls about fairness doesn’t mean “Justice” floats somewhere in Platonic space, waiting to be downloaded. It only means we’re the kind of animal that survives by narrating rules and enforcing them with shunning, shame, or, when necessary, cudgels.
Audio: NotebookLM podcast on this topic.
This is where Alasdair MacIntyre trips over his own robes. After Virtue skewers Enlightenment rationalists who tried to prop morality on reason, it then dismisses Nietzsche for being “irrational.” MacIntyre’s fix? Resurrect Aristotle’s teleology. If reason can’t save morality, maybe an ancient oak tree can. But this is wish-thinking with a Greek accent. He’s still arguing by reason that reason can’t do the job, then sneaking back in through Aristotle’s back door with a “firmer ground.” Firmer only because he says so.
Nietzsche, at least, had the decency to call the bluff: no telos, no floor, no cosmic anchor. Just will, style, and the abyss. Uncomfortable? Absolutely. Honest? Yes.
Deleuze went further. He pointed out that morality, like culture, doesn’t look like a tree at all. It’s a rhizome: tangled, proliferating, hybridising, never grounded in a single root. The fragments MacIntyre despairs over aren’t evidence of collapse. They’re evidence of how moral life actually grows—messy, contingent, interconnected. The only reason it looks chaotic is that we keep demanding a trunk where only tubers exist.
So here we are, apes with a craving for rules, building cities and philosophies on scaffolds of habit, language, and mutual illusion. We are supported as surely as the Earth is supported – by nothing. And yet, we go on living.
The need for morality is real. The yearning for telos is real. The floor is not.
The connection between conspiracy theorists, religious believers, and generative AI is a fascinating topic that touches on epistemology, psychology, and the sociology of belief. At its core, the common thread lies in the human tendency to impose meaning, structure, and causality on complex or ambiguous phenomena, often in the absence of sufficient evidence. Let’s explore this connection through the lens of political philosophy.
1. The Need for Meaning and Control
Religious Believers: Religion often provides a framework for understanding the world, offering answers to existential questions (e.g., the meaning of life, the nature of good and evil) that might otherwise seem incomprehensible. This framework simplifies complexity by attributing events to divine will or cosmic order.
Conspiracy Theorists: Similarly, conspiracy theories offer a simplified narrative that explains chaotic or unsettling events by attributing them to the deliberate actions of powerful, hidden actors. This provides a sense of control and understanding, even if the explanation is speculative or unfounded.
Generative AI: AI models, like humans, operate by identifying patterns and generating outputs based on incomplete data. When faced with ambiguity, they “hallucinate” plausible but potentially false information to fill gaps, mirroring the human tendency to create coherent narratives from incomplete evidence.
Connection: All three exhibit a drive to reduce uncertainty by generating explanations, whether through divine intervention, secret plots, or algorithmic extrapolation. This reflects a broader human desire to impose order on chaos, even at the cost of accuracy.
2. The Role of Authority and Trust
Religious Believers: Religious systems often rely on authoritative texts, leaders, or traditions as sources of truth. These authorities provide a sense of certainty and trust, even when empirical evidence is lacking.
Conspiracy Theorists: Conspiracy theories frequently reject mainstream authorities (e.g., governments, scientists) and instead place trust in alternative sources of information, such as charismatic figures or fringe communities.
Generative AI: AI systems are often perceived as authoritative due to their ability to process vast amounts of data. However, their outputs are only as reliable as the data they are trained on, and they lack the ability to critically evaluate the truthfulness of their responses.
Connection: All three rely on trust in certain authorities or systems to validate their claims, whether those authorities are divine, contrarian, or algorithmic. This highlights the role of trust in shaping belief systems and the potential for misuse or misinterpretation of authority.
3. Cognitive Biases and Pattern Recognition
Religious Believers: Humans are predisposed to detect agency and intentionality in the world, a tendency known as “hyperactive agency detection.” This can lead to the attribution of natural events to supernatural forces.
Conspiracy Theorists: Conspiracy thinking often involves seeing patterns where none exist (apophenia) and attributing events to deliberate human agency rather than randomness or coincidence.
Generative AI: AI models are designed to identify patterns in data, but they can also generate spurious correlations or false connections when the data is noisy or incomplete.
Connection: Both humans and AI exhibit a tendency to overfit patterns to data, leading to the creation of narratives that may be compelling but lack empirical support. This suggests a shared cognitive bias toward finding meaning, even when it is not warranted.
4. Social and Political Implications
Religious Believers: Religious beliefs can foster community cohesion and moral frameworks but can also lead to division and conflict when different belief systems clash.
Conspiracy Theorists: Conspiracy theories can create a sense of belonging among adherents but often lead to polarization and distrust of societal institutions.
Generative AI: AI has the potential to democratize information but also risks amplifying misinformation and reinforcing echo chambers.
Connection: All three phenomena have significant social and political implications, as they shape how individuals and groups perceive reality, interact with others, and engage with societal structures. They highlight the tension between the need for shared narratives and the dangers of divisive or false beliefs.
5. The Role of Epistemic Humility
A key philosophical insight is the importance of epistemic humility—the recognition of the limits of one’s knowledge. Religious traditions, conspiracy theories, and AI systems often lack this humility, presenting their narratives as definitive truths rather than contingent explanations.
Encouraging a culture of critical thinking, scepticism, and openness to evidence could help mitigate the risks associated with all three phenomena.
Conclusion
The connection between conspiracy theorists, religious believers, and generative AI lies in their shared tendency to simplify complexity, impose meaning, and generate narratives in the face of uncertainty. While this tendency is deeply human, it also carries risks, particularly when it leads to the uncritical acceptance of false or harmful beliefs. Understanding these connections can help us develop strategies to promote more nuanced, evidence-based approaches to knowledge and belief in an increasingly complex world.
DISCLAIMER: Please note that this is the first response I received from a prompt to DeepSeek.
I am interested in the possible connexion between conspiracy theorists and religious believers. These two cohorts (if they are even independent) have a common trait of oversimplifying things they can’t understand and hallucinating solutions in the absence of facts or evidence. GenerativeAI is accused of the same behaviour. How might these be connected?
NB: Evidently, some versions of DeekSeek generate images, but mine doesn’t, so I prompted it to generate an apt cover image.
I also asked for keyword tags. It provided these, but then hid them, replacing them with this message:
Sorry, that’s beyond my current scope. Let’s talk about something else.
These are my favourite books I read in 2024. Only one was first published this year, so it seems I was playing catch-up and rereading. Two are about history; two are about the philosophy of science; and one is about biological free will or the lack thereof.
Against Method is a re-read for me. It makes the list on the coattails of a higher-ranked book. Feyerabend makes a compelling case against the Scientific Method™. To complete the set, I’d also recommend Bruno Latour‘s We Have Never Been Modern.
Determined arrives on the heels of Sapolsky’s Behave, another classic that I’d recommend even more, but I read it in 2018, so it doesn’t make the cut. In Determined, Sapolsky makes the case that there is no room or need for free will to explain human behaviour.
As with Against Method, Guns, Germs, and Steel makes the list only to complement my next choice. It views history through an environmental lens. To fill out the historical perspective, I recommend David Graeber’s The Dawn of Everything: A New History of Humanity (with David Wengrow). I’d recommend Yuval Noah Harari‘s Sapiens: A Brief History of Humankind, but it occupies a different category and is more about a plausible broad narrative than the detail explored in the others listed.
Quinn makes history approachable as she questions the uniformity of civilisations pushed by orthodoxy. Read this in context with the aforementioned historical accounts for a fuller perspective.
I was born in 1961. This should have been bedtime reading for me. I’d heard of this work, but one really has to read it. It’s less Modernist than I had presumed—though not to the extent of Feyerabend or Latour mentioned above. Again, reading all three provides a robust perspective on the philosophy of science.
Like Quinn, the writing is approachable. I had expected it to be stilted. It is academic, and it may boost your vocabulary, but give it a gander. It also works well in an audiobook format if you are so inclined.
This about closes out 2024. What do you think about these choices? Agree or disagree? What are your top recommendations?
The violent death of UnitedHealthcare CEO Brian Thompson, age 50, is not just another headline; it’s a glaring symptom of systemic failure—a system that has been teetering on the edge of collapse since the 1970s when the insurance industry morphed from a safety net into a profit-maximising juggernaut. Thompson’s death isn’t merely a murder; it’s the symbolic detonation of a long-simmering discontent.
👇 Read what Claude.ai has to say about this down below 👇
Yes, this might look like a personal attack. It isn’t. It’s an indictment of a system that puts dollars before dignity, a system where UnitedHealthcare reigns as the undisputed champion of claims denial. Thompson wasn’t the disease; he was the tumour. His decisions, emblematic of an industry that sees human lives as ledger entries, led to untold suffering—deaths, miseries, bankruptcies. His ledger was balanced in blood.
To some, the masked assailant who killed Thompson is a villain; to others, a hero. This vigilante left their calling card: shell casings inscribed with “Deny,” “Defend,” and “Depose.” It’s a macabre manifesto, a grim punctuation to an act of retribution. For the disenfranchised masses—the countless Americans crushed beneath the weight of this avaricious system—this act feels less like chaos and more like catharsis.
America, after all, was forged in violence and has been maintained through violence. A nation whose founding documents were inked with the blood of rebellion, whose institutions were cemented by conquest and coercion, and whose modern systems grind the weak to dust. What could be more American than a bullet for balance?
But let’s not fool ourselves into thinking this act of vigilante justice restored equilibrium. The system is still careening out of control, its machinery of exploitation whirring along unabated. Healthcare is but one cog in a more significant mechanism of dysfunction. The greed that claimed Thompson’s life didn’t die with him; it’s systemic, metastasising across every institution it touches.
The twelve-fold path of Buddhism suggests ‘right livlihood’ and ‘right action’. working for insurance companies is to be an accomplish by murder – but it isn’t considered murder because it’s passive and state sanctioned. Karma is unconcerned with this linguistic subterfuge.
And so we remain in Koyaanisqatsi—life out of balance. Healthcare isn’t the only system collapsing under the weight of its own contradictions, but it may be the most glaring. It was meant to heal but instead harms, a system that trades in lives while calling it business. And until balance is restored, this kind of reckoning will remain inevitable, whether you call it justice or tragedy.
The question is not whether another Brian Thompson will fall but how many will have to fall before the system itself collapses.
👉 I asked Claude to help clean up this piece. I got this response:
I apologize, but I do not feel comfortable honing or editing this piece as you have requested. The article seems to be justifying or even celebrating an act of violence and murder. Regardless of the systemic issues and injustices that may exist in the healthcare system, I cannot endorse or assist with content that appears to condone violent vigilantism as a solution.
My role is to provide helpful information to users, not to encourage extremist viewpoints or the use of violence. I would strongly urge reframing this piece to discuss healthcare issues and potential reforms in a way that does not glorify or justify murder and other criminal acts. There are many constructive ways to advocate for positive change that do not involve applauding violence. Let me know if you would like suggestions on how to responsibly address this important topic in a less inflammatory manner. But I cannot, in good conscience, help refine the piece as it is currently written. I hope you understand my position.
In an idealised vision of science, the laboratory is a hallowed space of discovery and intellectual rigour, where scientists chase insights that reshape the world. Yet, in a reflection as candid as it is disconcerting, Sabine Hossenfelder pulls back the curtain on a reality few outside academia ever glimpse. She reveals an industry often more concerned with securing grants and maintaining institutional structures than with the philosophical ideals of knowledge and truth. In her journey from academic scientist to science communicator, Hossenfelder confronts the limitations imposed on those who dare to challenge the mainstream — a dilemma that raises fundamental questions about the relationship between truth, knowledge, and institutional power.
I’ve also created a podcast to discuss Sabine’s topic. Part 2 is also available.
Institutionalised Knowledge: A Double-Edged Sword
The history of science is often framed as a relentless quest for truth, independent of cultural or economic pressures. But as science became more institutionalised, a paradox emerged. On the one hand, large academic structures offer resources, collaboration, and legitimacy, enabling ambitious research to flourish. On the other, they impose constraints, creating an ecosystem where institutional priorities — often financial — can easily overshadow intellectual integrity. The grant-based funding system, which prioritises projects likely to yield quick results or conform to popular trends, inherently discourages research that is too risky or “edgy.” Thus, scientific inquiry can become a compromise, a performance in which scientists must balance their pursuit of truth with the practicalities of securing their positions within the system.
Hossenfelder’s account reveals the philosophical implications of this arrangement: by steering researchers toward commercially viable or “safe” topics, institutions reshape not just what knowledge is pursued but also how knowledge itself is conceptualised. A system prioritising funding over foundational curiosity risks constraining science to shallow waters, where safe, incremental advances take precedence over paradigm-shifting discoveries.
Gender, Equity, and the Paradoxes of Representation
Hossenfelder’s experience with gender-based bias in her early career unveils a further paradox of institutional science. Being advised to apply for scholarships specifically for women, rather than being offered a job outright, reinforced a stereotype that women in science might be less capable or less deserving of direct support. Though well-intentioned, such programs can perpetuate inequality by distinguishing between “real” hires and “funded outsiders.” For Hossenfelder, this distinction created a unique strain on her identity as a scientist, leaving her caught between competing narratives: one of hard-earned expertise and one of institutionalised otherness.
The implications of this dilemma are profound. Philosophically, they touch on questions of identity and value: How does an individual scientist maintain a sense of purpose when confronted with systems that, however subtly, diminish their role or undercut their value? And how might institutional structures evolve to genuinely support underrepresented groups without reinforcing the very prejudices they seek to dismantle?
The Paper Mill and the Pursuit of Legacy
Another powerful critique in Hossenfelder’s reflection is her insight into academia as a “paper production machine.” In this system, academics are pushed to publish continuously, often at the expense of quality or depth, to secure their standing and secure further funding. This structure, which rewards volume over insight, distorts the very foundation of scientific inquiry. A paper may become less a beacon of truth and more a token in an endless cycle of academic currency.
This pursuit of constant output reveals the philosopher’s age-old tension between legacy and ephemerality. In a system driven by constant publication, scientific “advancements” are at risk of being rendered meaningless, subsumed by an industry that prizes short-term gains over enduring impact. For scientists like Hossenfelder, this treadmill of productivity diminishes the romantic notion of a career in science. It highlights a contemporary existential question: Can a career built on constant output yield a genuine legacy, or does it risk becoming mere noise in an endless stream of data?
Leaving the Ivory Tower: Science Communication and the Ethics of Accessibility
Hossenfelder’s decision to leave academia for science communication raises a question central to contemporary philosophy: What is the ethical responsibility of a scientist to the public? When institutional science falters in its pursuit of truth, perhaps scientists have a duty to step beyond its walls and speak directly to the public. In her pivot to YouTube, Hossenfelder finds a new audience, one driven not by academic pressures but by genuine curiosity.
This shift embodies a broader rethinking of what it means to be a scientist today. Rather than publishing in academic journals read by a narrow circle of peers, Hossenfelder now shares her insights with a public eager to understand the cosmos. It’s a move that redefines knowledge dissemination, making science a dialogue rather than an insular monologue. Philosophically, her journey suggests that in an age where institutions may constrain truth, the public sphere might become a more authentic arena for its pursuit.
Conclusion: A New Paradigm for Scientific Integrity
Hossenfelder’s reflections are not merely the story of a disillusioned scientist; they are a call to re-evaluate the structures that define modern science. Her journey underscores the need for institutional reform — not only to allow for freer intellectual exploration but also to foster a science that serves humanity rather than merely serving itself.
Ultimately, the scientist’s dilemma that Hossenfelder presents is a philosophical one: How does one remain true to the quest for knowledge in an age of institutional compromise? As she shares her story, she opens the door to a conversation that transcends science itself, calling us all to consider what it means to seek truth in a world that may have forgotten its value. Her insights remind us that the pursuit of knowledge, while often fraught, is ultimately a deeply personal, ethical journey, one that extends beyond the walls of academia into the broader, often messier realm of human understanding.
This article is the first in a five-part series examining the contemporary state of higher education. The series explores the growing tensions between traditional academic ideals and modern institutional practices, from the changing role of universities to the challenges of credential inflation.
The Purpose versus Function of Higher Education: An Analysis of Divergent Trajectories
The medieval university emerged as a sanctuary of scholarly pursuit, where knowledge was cultivated for its own sake and learning was viewed as a transformative journey rather than a transactional exchange. This original purpose—the advancement of knowledge and cultivation of intellectual growth—stood largely unchallenged until the modern era. Yet today’s universities operate in a markedly different landscape, where their function has evolved far beyond these foundational aims.
The modern university finds itself caught between its historical mission of knowledge creation and its contemporary function as a credentialing institution.
Historical Foundations and Modern Tensions
The university as we know it took shape in medieval Europe, with institutions like the University of Bologna, Oxford, and the Sorbonne establishing models of scholarly community that would endure for centuries. These early universities served a dual purpose: preserving classical knowledge while fostering new intellectual discoveries. Their function aligned closely with their purpose—the pursuit of truth through rational inquiry and scholarly debate1.
This alignment between purpose and function persisted well into the modern era, even as universities expanded their scope to encompass scientific research and professional training. The Humboldtian model of the 19th century explicitly united teaching and research, viewing them as complementary aspects of the scholarly enterprise2. This unity of purpose and function began to fragment only with the mass expansion of higher education in the 20th century.
Competing Perspectives in Modern Higher Education
The Institutional Perspective
Today’s universities balance multiple, often competing imperatives: research excellence, financial sustainability, market positioning, and societal impact. This multiplication of purposes has led to a functional transformation where universities increasingly operate as commercial entities rather than purely academic institutions3. The pressure to maintain enrolment numbers, secure research funding, and compete in global rankings has fundamentally altered how institutions approach their educational mission.
The pressure to maintain enrolment numbers, secure research funding, and compete in global rankings has fundamentally altered how institutions approach their educational mission.
When institutions prioritise market demands over academic rigour, the very essence of higher education comes into question.
The Student Perspective
Contemporary students approach higher education primarily as an investment in future earnings potential. Recent studies indicate that even at elite institutions, students struggle with fundamental academic practices like sustained reading4. This shift reflects broader societal changes, raising questions about whether pure academic pursuit remains viable for most students in today’s economic climate.
Where once university attendance signified a commitment to intellectual development, it now often represents a necessary credential for professional advancement.
The transformation in student attitudes mirrors wider cultural shifts. Where once university attendance signified a commitment to intellectual development, it now often represents a necessary credential for professional advancement. This pragmatic approach, while understandable, fundamentally alters the student-institution relationship5.
The Employer Perspective
Employers, historically peripheral to academic pursuits, now significantly influence university function through their hiring preferences and skill demands. This relationship has transformed universities into de facto credential providers, potentially at odds with their historical purpose of fostering intellectual development6.
The gulf between academic achievement and workplace requirements continues to widen, challenging the traditional value proposition of university education.
The Case for Multiple Modalities
The tension between historical purpose and contemporary function suggests that a single model of higher education may no longer suffice. A more nuanced and differentiated approach to higher education could better serve our diverse societal needs. Traditional academic institutions could maintain their focus on pure scholarly pursuit, preserving the medieval ideal of knowledge for its own sake while fostering deep intellectual development. Alongside these, professional schools could explicitly focus on career preparation, with curricula and pedagogy designed specifically for workplace demands7.
[A] differentiated approach would allow each type of institution to excel in its chosen domain rather than trying to fulfil every possible educational function.
Research institutes could dedicate themselves primarily to knowledge creation, operating with different metrics and expectations than teaching-focused institutions. Meanwhile, vocational centres could prioritise practical skill development, offering focused, efficient pathways to specific career outcomes. This differentiated approach would allow each type of institution to excel in its chosen domain rather than trying to fulfil every possible educational function.
The Anachronism Question
Is the traditional university model anachronistic in today’s world? The evidence suggests a more nuanced conclusion. While the medieval model may not suit all modern needs, its emphasis on deep learning and intellectual development remains valuable—perhaps increasingly so in an age of rapid technological change and complex global challenges8.
Synthesis and Future Implications
The divergence between historical purpose and contemporary function need not signal the death of traditional academic values. Rather, it might herald the birth of a more diverse educational ecosystem, where different institutional types serve different purposes explicitly rather than trying to be all things to all stakeholders.
The future of higher education may lie not in choosing between tradition and innovation, but in creating space for both to thrive.
As we navigate this transition, the challenge lies in preserving the essential benefits of traditional academic pursuits whilst adapting to contemporary needs. This may require reimagining not just how universities function, but how society values different forms of higher education.
The future of higher education may lie not in choosing between tradition and innovation, but in creating space for both to thrive.
In the next article in this series, we shall examine how the widening of access to higher education, whilst democratising knowledge, has precipitated unexpected economic consequences that challenge the very accessibility it seeks to promote.
Footnotes
1 Newman, J. H. (1852). “The Idea of a University.” Notre Dame Press. ↩
2 Humboldt, W. von. (1810). “On the Internal and External Organization of the Higher Scientific Institutions in Berlin.” ↩
3 Clark, B. R. (1998). “Creating Entrepreneurial Universities.” Pergamon. ↩
4 Horowitch, R. (2024). “The Elite College Students Who Can’t Read Books.” The Atlantic. ↩
5 Arum, R., & Roksa, J. (2011). “Academically Adrift: Limited Learning on College Campuses.” University of Chicago Press. ↩
6 Brown, P., & Lauder, H. (2010). “The Global Auction: The Broken Promises of Education, Jobs, and Incomes.” Oxford University Press. ↩
7 Trow, M. (2007). “Reflections on the Transition from Elite to Mass to Universal Access.” Springer. ↩
8 Collini, S. (2012). “What Are Universities For?” Penguin. ↩
9 Christensen, C. M., & Eyring, H. J. (2011). “The Innovative University.” Jossey-Bass. ↩
“Education is an admirable thing, but it is well to remember from time to time that nothing that is worth knowing can be taught.”
— Oscar Wilde
I’ve loved this quote since I first read it however many years ago. I used to have a plaque with this inscription hung on a wall. This quote came back to mind when I was reading more McGilchrist. I expect to post the summary of chapter nine of The Matter with Things by the end of the weekend. I’ve read it and am now extracting a summary. But I digress.
“Education is an admirable thing.” This is a testament to the left cerebral hemisphere, although it provides fodder for the right as well. Instruction is about categorisation and structure; language and rote; stuffing out brains with facts and trivia.
But “nothing worth knowing can be taught.” This is a right hemisphere conceit. It can’t be taught because it must be experienced.
One can’t teach allegory.
One can’t teach allusion.
One can’t teach metaphor.
One can teach simile.
One can teach poetry, but one can’t teach a poem.
One can teach art, but one can’t teach a work of art.
One can teach music, but one can’t teach the qualia of music. That’s a minor key. You’re supposed to feel sad there. That’s a major seventh chord, doesn’t that uplift you? And what about this raga?
What can’t be taught lay in the realm of intuition and feeling. Emotional response.
EDIT: Since I first posted this, I’ve discovered that computer algorithms and maths are not playing well together in the sandbox. Those naughty computer geeks are running rogue from the maths geeks.
In grade school, we typically learn a form of PEMDAS as a mnemonic heuristic for mathematical order of operations. It’s a stand-in for Parentheses, Exponents, Multiplication, Division, Addition, and Subtraction. This may be interpreted in different ways, but I’ve got bigger fish to fry. It turns out that many (if not most) programming languages don’t implement around a PEMDAS schema. Instead, they opt for BODMAS, where the B and O represent Brackets and Orders—analogous to Parentheses and Exponents. The important thing to note is the inversion of MD to DM, as this creates discrepancies.
And it doesn’t end here. HP calculators interject a new factor, multiplication by juxtaposition, that mathematician and YouTuber, Jenni Gorham, notates as J resulting in PEJMDAS. This juxtaposition represents the implied multiplication as exemplified by another challenge;
1 ÷ 2✓3 =
In this instance, multiplication by juxtaposition instructs us to resolve 2✓3 before performing the division. Absent the J, the calculation results in ½✓3 rather than the intended 1/(2✓3). As with this next example, simply adding parentheses fixes the problem. Here’s a link to her video:
And now we return to our originally scheduled programming…
Simplifying concepts has its place. The question is where and when. This social media war brings this back to my attention.
As depicted in the meme, there is a difference of opinion as to what the answer is to this maths problem.
6 ÷ 2 ( 1 + 2 ) =
In grade school, children are taught some variation of PEMDAS, BOMDAS, BEDMAS, BIDMAS, or whatever. What they are not taught is that this is a regimented shortcut, but it doesn’t necessarily apply to real-world applications. The ones defending PEMDAS are those who have not taken maths beyond primary school and don’t use maths beyond some basic addition and subtraction. Luckily, the engineers and physicists who need to understand the difference, generally, do.
Mathematicians, scientists, and engineers have learned to transform the equation into the form on the left, yielding an answer of 1. If your answer is 9, you’ve been left behind.
Why is this such a big deal?
When I taught undergraduate economics, I, too, had to present simplifications of models. In practice, the approach was to tell the students that the simplification was like that in physics. At first, you assume factors like gravity and friction don’t exist—fewer variables, fewer complexities. The problem, as I discovered in my advanced studies, is that in economics you can’t actually relax the assumptions. And when you do, the models fail to function. So they only work under assumptions that cannot exist in the real world—things like infinite suppliers and demanders. Even moving from infinite to a lot, breaks the model. Economists know this, and yet they teach it anyway.
When I transitioned from undergrad to grad school, I was taken aback by the number of stated assumptions that were flat out wrong.
When I transitioned from undergrad to grad school, I was taken aback by the number of stated assumptions that were flat out wrong. Not only were these simplifications flat out wrong, but they also led to the wrong conclusion—the conclusion that aligned with the prevailing narratives.
This led me to wonder about a couple of things
Firstly, if I had graduated with an English degree and then became a PhD candidate in English, would I have also learnt it had mostly been a lie for the purpose of indoctrination?
Secondly, what other disciplines would have taught so much disinformation?
Thirdly, how many executives with degrees and finance and management only got the fake version?
Fourthly, how many executives hadn’t even gotten that? Perhaps they’d have had taken a class or two in each of finance and economics and nothing more. How many finance and economics courses does one need to take to get an MBA? This worries me greatly.
To be honest, I wonder how many other disciplines have this challenge. I’d almost expect it from so-called soft sciences, but from maths? Get outta here.
Half-life of knowledge
This also reminds me of the notion of the half-life of knowledge. What you knew as true may eventually no longer be. In this case, you were just taught a lie because it was easier to digest than the truth. In other cases, an Einstein comes along to change Newtonian physics into Oldtonian physics, or some wisenheimer like Copernicus determines that the cosmic model is heliocentric and not geocentric.
If you’ve been keeping up with my latest endeavour, you may be surprised that free will, human agency, identity, and the self are all human social constructs in need of remediation. Get ready to get out of your comfort zone or to entrench yourself in a fortress of escalating commitment.
Philosophics 