方法对比

	Conjunctural History	Historical Corpus Text Mining
领域	Economic History	Digital History
方法族	Process / pipeline	Process / pipeline
起源年份	1944	2013
提出者	Ernest Labrousse	Franco Moretti
类型	analytical-framework	text-analysis-pipeline
开创性文献	Labrousse, E. (1944). La crise de l'economie francaise a la fin de l'Ancien Regime et au debut de la Revolution. Presses Universitaires de France. ISBN: 9782130436201	Moretti, F. (2013). Distant Reading. Verso. ISBN: 9781781680841
别名	Conjoncture analysis, Cyclical economic history, Price-history of cycles, Labroussian conjunctural method	Distant reading, Computational historical text analysis, Macroanalysis of corpora, Corpus-scale historical NLP
相关	3	3
摘要	Conjunctural history studies the medium-term cyclical movements, the conjoncture, that occupy the middle layer of Braudel's tripartite time scheme, between the near-immobile longue duree and the rapid surface of events. Pioneered by Ernest Labrousse in his studies of eighteenth-century French prices, the method reconstructs decade-scale fluctuations in prices, wages, harvests, and production, then asks how these economic rhythms reverberate through society and politics. Labrousse showed that interlocking cycles of grain prices and agricultural revenue could converge into acute crises that strained the social order, contributing to the conditions for revolution. The conjoncture is thus neither the slow structure nor the fleeting event but the oscillating economic mood of a period. By charting these waves with quantitative series and linking their peaks and troughs to social tension, popular unrest, and political rupture, conjunctural history offers a bridge between economic measurement and the explanation of historical change.	Historical corpus text mining applies computational methods to thousands or millions of historical documents at once, seeking macro-scale patterns that close reading of individual texts could never reveal. Associated above all with Franco Moretti's program of distant reading, the approach treats large bodies of text, newspapers, parliamentary records, novels, correspondence, as data to be measured rather than works to be interpreted one by one. By counting word frequencies, computing weighted term importance, fitting topic models, and tracking how vocabulary shifts across decades, researchers can chart the rise and fall of concepts, the diffusion of ideas, and the changing texture of public discourse over long spans. The method is explicitly quantitative and aggregative: its claims concern populations of documents, not exemplary passages. Adapting modern natural-language processing to historical material, however, requires confronting archaic spelling, OCR noise, and shifting word meanings. Done carefully, corpus text mining turns vast unread archives into evidence about how language, and the thought it carries, evolved historically.