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Cited by 803

The DOI matches its Crossref record; 19 of the 19 references it cites were found in a registry or an index.

Making News by Doing Work: Routinizing the Unexpected

Tuchman, Gaye·1973·American Journal of Sociology

Abstract

Noting that, on the one hand, variability in raw materials impedes routinizations and that, on the other hand, organizations impose routines to control the flow of work, this article explores two related questions: (1) How can an organization routinely process unexpected events? (2) How do newsmen decrease the variability of events that form the raw material of news? The article examines five classifications used by newsmen to distinguish among events-as-news. First, the article analyzes these classifications as definitional "categories." That approach failingfor specified reasons, it views these same classifications as typifications. This analysis stresses the extent to which typification of raw materials arises out of the requirements of the organizational structure of news work. It pays special attention to some practical problems concerning the control of work. It also discusses the pamifications that arise for the news organizations from the manner in which an event occurs. Some implications of the analysis are discussed.

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Metadata

Authors
Tuchman, G.
Venue
American Journal of Sociology
Published
1973-07
Volume
79
Issue
1
Pages
110-131
ISSN
  • 0002-9602 (ISSN-L)
  • 1537-5390
OpenAlex
W2078550151
Cited by
803
References
19
FWCI
113.70
Type
journal article
Language
en
Open access
closed

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CoChat. (2026). Citation check: Making News by Doing Work: Routinizing the Unexpected. https://app.cochat.ai/papers/10.1086/225510
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Tuchman, G. (1973). Making News by Doing Work: Routinizing the Unexpected. American Journal of Sociology. https://doi.org/10.1086/225510

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Bibliographic data: Semantic Scholar, OpenAlex, Crossref.

Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).

Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.