Breadcrumb

QoG lunch seminar with Jørgen Møller

Society and economy

Social Science History: Principles and Procedures for Processing Historical Evidence

Seminar
Date
31 May 2023
Time
12:00 - 13:00
Location
Stora Skansen (room B336), Sprängkullsgatan 19

Participants
Jørgen Møller, Professor, Department of Political Science, Aarhus University
Good to know
The QoG institute regularly organizes seminars related to research on Quality of Government, broadly defined as trustworthy, reliable, impartial, uncorrupted and competent government institutions.

All seminars are held in English unless stated otherwise.
Organizer
The Quality of Government Institute

Abstract:

Social scientists increasingly engage in historical analysis, sometimes of periods very far back in time, sometimes of more recent periods. Embracing the work of historians and archeologist, the validity of social scientists’ evidence claims depends on the accuracy of the historical interpretations of these secondary sources, irrespective of which method of data analysis is used to process the information. This book starts by emphasizing that “history” should be recognized as an – often speculative and incomplete – interpretation of bygone times and places, which we can no longer know for sure. Much of what we commonly believe about the past depends on narrative sources written long after the fact and colored by later circumstances and points of view, on outdated historical research, or on singular and often biased contemporary sources. Discussing how to respond to this challenge, we call for a shift of cognitive style: treating the work of historians in a more self-conscious and critical way, and clearly signaling this when we write up our research. To do this, social scientists must also consider more carefully the biases that affect historical work and the biases that affect how they themselves approach and process this work. We develop and illustrate a set of criteria that enables social scientists to address these issues – and hence to enlist historical data in a way that is transparent and diminishes biases.