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Language Bias in Accident Investigation

Author

  • Crista Shannon Vesel

Summary, in English

This paper examined the language and content of the U.S. Forest Service's Serious Accident Investigation Guide (SAIG), which is used to investigate what the organization terms 'serious wildland fire accidents'. The purpose of research was to identify whether the language in the guide was objective, or if it biased the accident analysis process and conclusions of the accident report.
Qualitative research included philosophic, paradigmatic, and linguistic analyses of the 2001 and 2005 editions of the SAIG. Phone and/or questionnaire interviews were conducted with six current or former Forest Service personnel, who were familiar with the SAIG, versed in the use of the guide to complete accident reports, or familiar with the case study. This data was used to validate language biases and to determine what affects these might have on the report. The Thirtymile Fire Accident Report was used as a case study, to understand how the Forest Service and greater society may be affected by the language of accident reports.
Results affirmed that language bias exists in the SAIG and that it does affect accident analysis. The SAIG influences investigators to apply linear, hindsight biased, 'cause and effect' reasoning toward human actors in the event. The guide’s use of agentive descriptions, binary opposition, and the active verb voice creates a seemingly exclusive causal attribution toward humans. Objective analysis was found to be impossible, using the SAIG's language and report structure. This stands in contrast to the agency's goal of accident prevention. It is recommended that more research be done on the language and structure of accident investigation guidance, to help determine what changes may be necessary to align espoused values of prevention and organizational response to accidents.

Publishing year

2012

Language

English

Document type

Student publication for Master's degree (one year)

Topic

  • Social Sciences

Keywords

  • human causal attribution
  • SAIG
  • Serious Accident Investigation Guide
  • USFS
  • Forest Service
  • accident investigation
  • accident report
  • agentive language
  • FLMU06

Supervisor

  • James M. Nyce