People Trafficking and Smuggling Crimes in Australia: A Critical Analysis of State Intent
Bibliographic record
Abstract
Abstract This article’s objective is to expose the rhetorical source of the heavy irony in Australia’s immigration detention regime. The observer might wonder why an isolated and vast land could be so concerned at, and afraid of, small groups of “boat people.” Therefore, the paper poses the question as to what reasoning and public policy purposes might underlie the successful public rhetoric vilifying “boat people,” creating the construct of “people smuggling” and demanding military operations to “turn back the boats.” It tries to correlate with a likely state desire to resurrect the old laws of attainder, civil death and outlawry, in order to create a slave-class of displaced migrants, for solely state interests and purposes. In addressing the question structurally, discussion begins with a brief look at the Australian law. Argument then concentrates on the originating negotiations in the international high councils. After this, the article looks at instances of people smuggling rhetoric in Canada, also addressing briefly the United States law. Then there is a section on modern rhetorical analysis, which argument tries to use to explain what might underlie these government methods. The paper briefs the reader on the old laws of civil death, outlawry and attainder in Australia, with a view to a contextual assessment as to whether they are really what underlie the draconian outcomes of Australia’s human trafficking and people smuggling laws and policies. The research outcome will likely suggest that conveniences to the state such as efficiency in policing, removing likely political opposition from new arrivals, avoiding any dilution of the local culture and skirting unwanted international rights are most likely to be the real state intent.
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 | 0.011 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.016 | 0.030 |
| Scholarly communication | 0.010 | 0.007 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.003 | 0.007 |
| Insufficient payload (model declined to judge) | 0.003 | 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".