IB Philosophy 6.3.1 Science and Values Notes

The Value-Free Ideal

1. As discussed in 6.1.1, science aspires to objective knowledge of the natural world through observation, experiment, and measurement. A central question in the philosophy of science is whether this process can, or should, be entirely free of value judgements.
2. Value-free ideal holds that scientific inquiry must proceed without contamination from the personal beliefs, social commitments, or political agendas of individual scientists.
   1. On this view, the only legitimate considerations guiding research are whether a theory matches the evidence (empirical adequacy) and whether its reasoning is internally consistent (logical consistency).
3. Defenders of the value-free ideal argue that objectivity is the defining virtue of science, distinguishing it from ideology, religion, and pseudoscience.
4. As explored in 6.1.2, Karl Popper’s falsificationism reinforced this view by insisting that genuine science submits its claims to rigorous, value-neutral testing.
5. The logical positivists, a group of early twentieth-century philosophers who believed only scientifically testable claims have genuine meaning, were among the strongest advocates of value-free science.
6. Thinkers in the Vienna Circle argued that meaningful statements about the world must be empirically verifiable, leaving no room for value-laden claims within scientific discussion.
   1. However, the value-free ideal faces a fundamental challenge: scientists must constantly make choices about which problems to investigate, which methods to employ, and which standards of evidence to accept.
   2. These choices inevitably reflect background assumptions and priorities that go beyond pure logic.
7. Thomas Kuhn's account of scientific revolutions showed that science operates within paradigms shaped by the shared values of scientific communities.
   1. Choosing between paradigms is not a purely logical process: scientists also weigh simplicity, fruitfulness (does it open up new questions?), and explanatory scope (how much of the world can it explain?).
   2. The question is not simply whether individual scientists hold personal biases, but whether the structure of scientific inquiry itself requires commitments to certain values.
   3. If so, the value-free ideal may be not just unrealized, but unrealizable in principle.
8. This tension connects to the broader unit question: should scientists be held morally responsible for the uses of their inventions?
   1. If values inevitably shape scientific practice, then the responsibility of scientists extends beyond mere accuracy to include reflection on the social consequences of their work.
9. Understanding the relationship between science and values is therefore essential for evaluating the authority and trustworthiness of scientific knowledge.

Constitutive and Contextual Values

1. The most influential contemporary analysis of science and values comes from Helen Longino, who distinguishes between constitutive values and contextual values. This distinction provides a framework for understanding precisely how values enter scientific practice and whether their presence undermines scientific objectivity.
2. Constitutive values are those internal to science itself: empirical accuracy, internal consistency, simplicity, breadth of scope, and fruitfulness in generating new research. These values guide decisions about theory choice and methodology and are widely accepted as essential to good science.
3. Contextual values, by contrast, originate outside science in the broader social, cultural, and political environment. These include moral commitments, economic interests, political ideologies, and cultural assumptions.
4. The value-free ideal demands that contextual values be excluded from the core reasoning of science.

Longino's Argument for Social Objectivity

1. Longino argues that this strict separation is impossible in practice. Every experiment rests on background assumptions (the unstated beliefs scientists bring to their work in order to interpret what the data means), and these assumptions are themselves shaped by the wider social and cultural context.
2. For instance, research on intelligence has historically been influenced by racial and gender biases embedded in the assumptions researchers bring to their work.