A tainted trade? Moral ambivalence and legitimation work in the private security industry1
Bibliographic record
Abstract
The private security industry is often represented - and typically represents itself - as an expanding business, confident of its place in the world and sure of its ability to meet a rising demand for security. But closer inspection of the ways in which industry players talk about its past, present and future suggests that this self-promotion is accompanied by unease about the industry's condition and legitimacy. In this paper, we analyse the self-understandings of those who sell security - as revealed in interviews conducted with key industry players and in a range of trade materials - in order to highlight and dissect the constitutive elements of this ambivalence. This analysis begins by describing the reputational problems that are currently thought to beset the industry and the underlying fears about its status and worth that these difficulties disclose. We then examine how security players seek to legitimate the industry using various narratives of professionalization. Four such narratives are identified - regulation, education, association and borrowing - each of which seeks to justify private security and enhance the industry's social worth. What is striking about these legitimation claims is that they tend not to justify the selling of security in market terms. In conclusion we ask why this is the case and argue that market justifications are 'closed-off' by a moral ambivalence that attaches to an industry trading in products which cannot guarantee to deliver the condition that its consumers crave.
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 | 0.020 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.025 | 0.129 |
| Scholarly communication | 0.021 | 0.013 |
| Open science | 0.001 | 0.009 |
| Research integrity | 0.007 | 0.008 |
| Insufficient payload (model declined to judge) | 0.004 | 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".