Investigating vicarious trauma: dramaturgical challenges for qualitative research
Bibliographic record
Abstract
Purpose This article examines personal performances of vicarious trauma (VT) related to the Ocean Ranger Disaster. It investigates the extent to which the self is at stake in passionate storytelling about tragic consequences of extreme work. Design/methodology/approach Dramaturgical concepts of self-presentation and impression management are used as a qualitative lens to provide an alternative view of published trauma stories arising from emotional research interviews. Findings The catastrophic disaster created secondary traumatization for families and friends of extreme workers lost at sea. This article shows that research interviews of these disaster survivors are opportunities for participants to engage in dramatic storytelling. The paper also reflects on related (problematic) storytelling by the trauma researcher. Research limitations/implications The article provides a theory illustration using dramaturgy as an alternative theoretical perspective to document previously under-appreciated aspects of the Ocean Ranger case. The discussion causes us to think about research interviews in a way that past research would not normally suggest. Social implications The Ocean Ranger Disaster continues to be a remarkable source of sorrow for the people of Newfoundland. This research provides a needed contrast to the numerous positivist, and overwhelmingly technological, studies of the disaster. Originality/value The research tradition of dramaturgy is a useful lens to apply to the expanding field of trauma studies. VT is rarely a subject of direct discussion in the management and organization studies (MOS) literature. This paper is among the first to consider storytelling interviews from a VT perspective.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.163 | 0.141 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.006 | 0.009 |
| Science and technology studies | 0.017 | 0.041 |
| Scholarly communication | 0.017 | 0.016 |
| Open science | 0.005 | 0.018 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".