About us

The Measurement Lab examines the social, cultural, political and historical forces that shape the meaning and function of measurement in medicine, health and health sciences. 

Theoretical Directions

Academic work on measurement comes from a range of disciplinary approaches, including history, sociology, science and technology studies, and philosophy. This work has shown that measurement data is grouped in a way that reifies the categories chosen, often cementing gender and racial biases. These divisions are perpetuated as measurements are standardised and replicated. Such categorisation processes can elevate biological divisions and consequently draw attention away from the social, structural, and environmental factors that truly create health inequities between groups. The implications of these measurement problems as they apply to health more broadly forms the focus of the Measurement Lab.  

We are open to and hoping to work with scholars working in the following key areas:

  • Measurement in relation to a specific condition
  • Measurement tools
  • Measurement categories, concepts, and standards
  • Inequalities
  • Race
  • Gender
  • Mental health
  • Neurodiversity
  • Clinical practices
  • Histories of measurement
  • Social construction of measurement, standards and categories

The Lab embraces all of these topics and has picked two focal starting points: 

  1. Neurodiversity and mental health. We explore measures of normality, diversity, and function in mental health, with a specific focus on neurodiversity. The idea of normality implies measurement, and the mental domain is (or seems) closed to intersubjective access. We explore the construction of notions of normality and typicality in mental, cognitive, neurological, and emotional contexts.

  2. Population health. We try to understand the gap between technologies and techniques designed to measure health at the population level, and the concepts available in epidemiology, philosophy, and elsewhere to understand these measures. Without this understanding, clinical and public health policies cannot be securely based on population-level data, and individual experiences are apt to become hidden.