When Method Becomes Metaphysics: How Science Came to Be Treated as the Supreme Arbiter of Truth
By Zaky Jaaar
Modern education, research governance, and quality assurance increasingly operate on an unspoken assumption: that science—understood as measurement, prediction, and method—is the best, and often the only, reliable arbiter of truth. This assumption rarely appears as an explicit philosophical claim. Instead, it emerges cumulatively through a set of maxims that feel intuitively “obvious,” pragmatic, and neutral. Yet when taken together, these maxims form not science, but scientism—the belief that scientific method exhausts what it means to know.
This essay examines those maxims, explains why each can fail, and argues that the elevation of science to a total epistemology has serious consequences for education, especially in fields concerned with meaning, creativity, and judgment.
The Rise of Method as Authority
Science has earned its authority through extraordinary success. Its methods have transformed medicine, technology, and our understanding of the physical universe. The problem arises not from science itself, but from the philosophical inflation of its methods into universal criteria of truth.
Michael Polanyi warned of this shift decades ago, arguing that modernity increasingly mistakes formal method for knowledge itself, ignoring the tacit, personal, and interpretive dimensions that make discovery possible in the first place (Polanyi 1958). What began as methodological humility—“let us be careful, systematic, and testable”—gradually hardened into metaphysical certainty.
The Maxims That Built Scientism
Several maxims, often unstated, underpin the belief that science is the supreme arbiter of truth.
First, there is the assumption that what is real is what can be observed. Yet many real entities—mathematical objects, intentions, moral obligations, social norms—are not observable in any straightforward sense. Even in science, observation is theory-laden; we never observe “raw reality,” only reality interpreted through conceptual frames (Kuhn 1962).
Closely related is the claim that what cannot be measured cannot be known reliably. This collapses knowledge into quantification. Yet skilled judgment in medicine, law, teaching, or design relies on qualitative discernment refined through practice, not numerical measurement alone (Schön 1983). Reliability does not require numbers; it requires trained perception.
Another maxim insists that objectivity requires detachment. But complete detachment often impoverishes understanding. In fields such as anthropology, clinical medicine, and education, engagement is not a bias to be eliminated but a condition of insight. Polanyi famously argued that all knowing involves personal participation, even in the sciences (Polanyi 1958).
Similarly, quantification is often treated as superior to qualification. Numbers carry authority, but they abstract away meaning. Every act of quantification presupposes qualitative judgments about what matters enough to count. As Heidegger warned, calculation can eclipse understanding when it becomes an end in itself (Heidegger 1977).
Repeatability is often taken as a guarantee of truth, yet many true phenomena are non-repeatable: historical events, evolutionary pathways, creative breakthroughs, and personal transformations. Repeatability tests stability under controlled conditions, not truth in all domains.
The assumption that causality is linear and isolatable also breaks down in complex systems. Climate, ecosystems, learning, and cognition exhibit nonlinear causation and emergence. Isolating variables may destroy the very phenomenon one seeks to understand (Morin 2008).
Another powerful maxim is that explanation equals understanding. But to explain neural correlates of love is not to understand love; to model learning outcomes is not to understand learning. Explanation answers “how,” while understanding often concerns meaning, value, and purpose (Biesta 2010).
Prediction is frequently treated as the highest test of knowledge. Yet many domains do not aim at prediction: ethics, education, history, and design seek judgment, interpretation, and wisdom. Predictive power alone cannot determine what is worth pursuing or why.
Modern narratives also assume that progress is cumulative and linear. Thomas Kuhn showed that scientific development proceeds through paradigm shifts that reframe reality, sometimes losing as well as gaining explanatory power (Kuhn 1962). Later is not simply better; it is different.
Another dangerous belief is that method guarantees truth. Methods are tools, not truth-machines. Applying the wrong method rigorously produces rigorously misleading results. Good judgment precedes good method, not the other way around.
The idea that values contaminate facts is equally untenable. Values shape research questions, funding priorities, definitions of success, and interpretations of data. Science is value-laden at its foundations, even if it strives for procedural objectivity (Longino 1990).
Expert consensus is often equated with truth, yet history shows that consensus can be wrong, delayed, or constrained by institutional power. Consensus is a social achievement, not an ontological guarantee.
Finally, there is the belief that all legitimate knowledge can be translated into scientific terms. But translation often transforms or destroys what it seeks to preserve. Justice, beauty, meaning, faith, and wisdom lose their nature when reduced to variables. As McGilchrist argues, reduction changes the world it claims merely to describe (McGilchrist 2019).
From Science to Scientism
Each maxim is locally useful. Collectively, they produce a worldview in which what is measurable is real, what is unmeasurable is suspect, and what cannot be predicted is irrelevant. This is not science; it is scientism.
In education, this worldview manifests in outcome fixation, metric saturation, and audit cultures that privilege what can be mapped and reported over what can be understood and cultivated. Bloom’s taxonomies and Outcome-Based Education, when rigidly applied, inherit these assumptions by treating learning as predictable, classifiable, and fully specifiable in advance.
Yet the most meaningful learning—especially in science itself—often arises from uncertainty, intuition, error, and surprise.
Conclusion: Toward Epistemic Humility
The question is not whether science is powerful—it undeniably is. The question is whether it should be asked to adjudicate all truth. When method becomes metaphysics, education risks mistaking control for understanding and measurement for meaning.
What is needed is not less science, but greater epistemic humility: an acknowledgment that different kinds of questions require different kinds of knowing. Science is indispensable—but it is not sovereign.
(This article is produced with AI assistance)
References
Biesta, G. (2010). Good Education in an Age of Measurement. Boulder: Paradigm Publishers.
Heidegger, M. (1977). The Question Concerning Technology and Other Essays. New York: Harper & Row.
Kuhn, T. S. (1962). The Structure of Scientific Revolutions. Chicago: University of Chicago Press.
Longino, H. (1990). Science as Social Knowledge. Princeton: Princeton University Press.
McGilchrist, I. (2019). The Master and His Emissary. New Haven: Yale University Press.
Morin, E. (2008). On Complexity. Cresskill, NJ: Hampton Press.
Polanyi, M. (1958). Personal Knowledge: Towards a Post-Critical Philosophy. Chicago: University of Chicago Press.
Schön, D. A. (1983). The Reflective Practitioner. New York: Basic Books.
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