Notes on Soft vs. Hard Science and Operationalization

The soft sciences (e.g., political science, psychology) vs hard sciences debate: Do soft sciences count as science and deserve a place beside chemistry and physics?
The National Academy of Sciences (NAS) episode (Lang vs Huntington) centers this question, highlighting tensions between political judgments and scholarly qualifications.
NAS: an advisory body to government; membership decisions are supposed to be based on scholarly merit, not politics.

Science = the enterprise of explaining and predicting natural phenomena by continually testing theories against empirical evidence.
This definition is broader than lab stereotypes; not all science yields decimal-precise measurements or controlled experiments.
Soft sciences cover ecology, evolution, animal behavior, psychology, economics, history, government—despite lacking perfect measurement conditions.

Core issue: How to operationalize a concept—turn abstract ideas into measurable indicators.
Necessary for comparing evidence with theory; without operationalization, measurement of variables like political instability or social frustration is ill-defined.
Operationalization becomes more challenging as complexity and uncontrolled variables rise.

Mathematics (hard science to start): the concept of "many" requires numbers; e.g., Gimi villagers with two root numbers, iya = 1 and rarido = 2, to build larger numbers.
Chemistry: identify measurable properties (weight, absorption) and use instruments; example: measuring sugar concentration via enzymatic reaction leading to a color change read by a spectrophotometer.
Ecology: habitat complexity; develop a single index (foliage height diversity index) measuring how foliage density varies with height to explain bird species richness.
Clinical Psychology: attitudes toward cancer; develop scales by clustering related statements, validate across contexts, and link attitudes to behaviors (e.g., frankness with patients related to views on early diagnosis and treatment).

Huntington: credentials (president of the American Political Science Association, Harvard professorship, acclaimed books) and broadly favorable regard from NAS members.
Lang: new NAS member with a focus on pure mathematics; opposed Huntington, accusing use of "pseudo mathematics" in the social sciences;
Process: NAS requires two-thirds support to sustain a candidate after debates; Huntington failed to secure this; Lang actively challenged within NAS.
Controversies debated: Huntington's CIA connections, Vietnam War work, and use of government advisory work; politics vs scholarly merit in NAS decisions.
Core takeaway: while politics entered debates, the central issue was how soft sciences are measured and validated.

Lang's concern highlights the central problem of operationalization: how to measure social frustration or political instability.
The broader point: many social phenomena cannot be measured with the precision of hard sciences, yet they can be studied rigorously with indirect but valid methods.
The labels soft vs hard are misleading; many soft-science problems are intellectually challenging and central to NAS's mission of informing public policy.
NAS’s role in advising government makes it crucial to include hard and soft sciences; excluding soft sciences would impede addressing real-world issues (e.g., stability, well-being, modernization).

Operationalization is essential across all sciences; its difficulty grows with complexity and fewer controlled variables.
Soft sciences can be as scientifically rigorous as hard sciences, and the distinction should not exclude them from prestigious scientific communities.
Our survival and policy effectiveness depend on understanding human behavior and social dynamics, not just abstract mathematical results.

ext{Correlation between frustration and instability (62 countries): } 0.50 \
ext{NAS members: } >1500 \
ext{New NAS members per year: } \approx 60 \