Introduction: Policing and Security Frontiers
Bibliographic record
Abstract
Policing and security are subjects that are central to criminology. Much criminological attention is paid to public police as well as contract private security agencies and guards. Yet there are emergent and other neglected forms of police and security provision on the frontiers of scholarship and practice. These range from brightly coloured ambassador patrols engaging urbanites on the streets, to community safety officers removing graffiti and quelling nuisances just beyond main police beats, to conservation officers walking obscure trails to look for homeless people's camps in the farthest reaches of urban parks, to mostly hidden corporate security personnel in public government, and to public police sponsors drawn from private corporations operating in the shadows. The idea of the ‘next frontier’ in policing or law enforcement is often invoked to signify something historically unique. Several criminologists have invoked the notion of frontier but usually only descriptively to refer to a space (McDonald, 1995; Hayward, 2004; McCulloch, 2004; Scott et al, 2007; Carrington et al, 2010). So it begs the question, what should ‘frontier’ mean for criminology and for studies of policing and security? Geographers and political scientists have long distinguished geopolitical frontiers from borders (Newman, 2003, 2006), the latter denoting a rigid, clear-cut nation-state boundary. Since the 1990s, a multidisciplinary critical border studies literature has emerged to challenge a static definition of the border (Kolossov, 2005; Parker and Vaughan-Williams, 2012; Brambilla, 2015). For example, Brambilla (2015: 15) identifies a ‘move away from’ borders ‘as naturalised and static territorial lines’, arguing for a notion of ‘borderscapes’, one of several notions that share some features with the notion of frontier, but nonetheless differ from it (see Geiger, 2009). Critical geographers have sought to decouple the notion of frontier from colonial historical accounts of ‘settler societies’ and ‘American exceptionalism’, arguing the ‘frontier notion was ethnocentric in the extreme’ (Geiger, 2009: 17) and reclaim it as a general analytical concept although one which keeps reference to the nation-state. While geographers have written on frontiers, they have rarely provided in these accounts direct and detailed focus on policing and security practices. A frontier is more mobile, less secured and more ambiguous than a traditionally understood border.
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 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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.006 | 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 teacher head, 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".