Scandalising contempt in Australia: Dead? Dying? In much danger? ... (not!)...
Bibliographic record
Abstract
The noughties have seen a sedes of scandalising the court contempt cases across Australia which, although not prolific in number; may still surprise those who have relegated this head of contempt to the back burner. This paper examines important lessons that may be gleaned from these cases regarding the current and future state of scandalising contempt in Australia. It also reflects on reasons why this offence appears to be doing so well in AUstralia compared with England, Canada and the United States (although not so much, it seems, with New Zealand). It may be that there is less cause for alarm than may appear at first sight. But, then again . .. At the end of the day, the analysis points to the need for more contemporary research and directions in this regard are suggested.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.017 | 0.018 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.005 | 0.016 |
| Insufficient payload (model declined to judge) | 0.005 | 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".