Corporate Security, Licensing, and Civil Accountability in the Australian Night-Time Economy
Bibliographic record
Abstract
Security arrangements in the night-time economy are linked to an increased range of restrictive liquor licensing regulations aimed at minimizing the prospect of alcohol-related harm, violence, and the legacies of deregulated trading adopted throughout most Australian states since the mid-1970s (Zajdow, 2011). As a key method of mitigating private business losses and maximizing profits, corporate security involves a disparate series of in-house or subcontracted arrangements to address both the problem of violence and the risk of state-imposed fines for breaches of alcohol service requirements. These processes operate in conjunction with mandatory private security licensing requirements applicable to all ‘crowd controllers’ or ‘bouncers,’ security companies, and personnel undertaking risk assessments and other knowledge work associated with loss prevention (Lippert et al., 2013). While the precise number and roles of corporate security personnel services are ill-defined and poorly understood in light of these multiple regulatory arrangements, much Australian research focuses on the broader implications of the activities of private bouncers as a complementary adjunct to order maintenance and violence prevention initiatives undertaken by the public police. This tendency overlooks a complex series of in-house and subcontracted corporate loss prevention and profit maximization arrangements within the Australian night-time economy. These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.012 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 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".