The ‘digital death knock’: Australian journalists’ use of social media in reporting everyday tragedy
Bibliographic record
Abstract
Newspapers regularly publish stories about people who have died suddenly or in unusual circumstances and the effect of these deaths on families and communities. The practice by which a journalist writes such a story is called the ‘death knock’; the journalist seeks out the deceased’s family to interview them for a story about their loss. The death knock is challenging and controversial. It has been criticized as an unethical intrusion on grief and privacy and shown to have negative effects on bereaved people and journalists. It has also been defended as an act of inclusion, giving the bereaved control over stories that may be written anyway, and a form of public service journalism that can have benefits for families, communities and journalists. Traditionally a knock on the door, the death knock is also done via phone and e-mail, and recently, in a practice termed the ‘digital death knock’, using social media. This article reports on the findings of a 2021 survey of Australian journalists and their current death knock practice and it will do this within the framework of research in the United States, the United Kingdom and Canada. In these countries, journalists are doing the ‘digital death knock’ because of time and competition pressures and available technology; however, this raises ethical concerns about their reproduction of social media material without the permission or knowledge of its owners. This article will discuss the extent to which social media has impacted death knock practice in Australia.
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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 | 0.015 | 0.068 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.010 | 0.008 |
| Scholarly communication | 0.009 | 0.006 |
| Open science | 0.001 | 0.009 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".