Corporate Security, Licensing, and Civil Accountability in the Australian Night-Time Economy
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 it