Grudge Spending: The Interplay between Markets and Culture in the Purchase of Security
Bibliographic record
Abstract
In the paper, we use data from an English study of security consumption, and recent work in the cultural sociology of markets, to illustrate the way in which moral and social commitments shape and often constrain decisions about how, or indeed whether, individuals and organizations enter markets for protection. Three main claims are proffered. We suggest, firstly, that the purchase of security commodities is a mundane, non-conspicuous mode of consumption that typically exists outside of the paraphernalia of consumer culture – a form of grudge spending. Secondly, we demonstrate that security consumption is weighed against other commitments that individuals and organizations have and is often kept in check by these competing considerations. We find, thirdly, that the prospect of consuming security prompts people to consider the relations that obtain between security objects and other things that they morally or aesthetically value, and to reflect on what the buying and selling of security signals about the condition and likely futures of their society. These points are illustrated using the examples of organizational consumption and gated communities. In respect of each case, we tease out the evaluative judgements that condition and constrain the purchase of security among organizations and individuals and argue that they open up some important but neglected questions to do with the moral economy of security.
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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 | 0.004 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 0.006 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".