‘What will the lawyers say?’: Australian newsroom perspectives on journalism ethics and naming criminal suspects in a digital world
Bibliographic record
Abstract
This article examines journalism ethics and identifying criminal suspects in Australian news coverage. This study builds off our previous research, which established that naming is so commonplace, it is occurring on a daily basis in the state of Victoria, even in cases with little public interest justification. A survey of 410 Australian news media professionals, as well as twelve semi-structured interviews, found journalists believe naming is an ethical decision, but it is not high on their agenda with naming treated as their default position. Media lawyers play a key role in newsroom naming practices with the legal strongly influencing what is deemed ethical. These findings are significant because it is the first empirical data from Australia and the findings are in stark contrast with news reporting practices in other countries. This article argues for stronger ethical guidelines in a digital news media environment where naming is now global and forever.
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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 | 0.046 | 0.084 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.019 | 0.038 |
| Scholarly communication | 0.022 | 0.012 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.010 | 0.011 |
| 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".