Security or Safety: Quantitative and Comparative Analysis of Usage in Research Works Published in 2004–2019
Bibliographic record
Abstract
This article is devoted to the statistical analysis of security and safety frequency in the context of categories connected with social institutions and personality features in research works from 2004-2019. Research was based on the following methods: quantitative analysis of safety frequency in the context with coded "categories" related to social institutions and personality features; analysis was conducted with computer-assisted content analysis QDA Miner Lite v. 1.4 and Fisher's F-test. An analysis of 1157 works showed that the terms "security" and "safety" were quantitatively more frequent when used with concepts related to social institutions than with concepts related to personality features. In our opinion, this qualitative trend shows the prevailing significance of social aspects of security over its personal (psychological) traits for research analysis and practical social aspects. The priority usage of the terms "security" and "safety" can be related to the securitization of society, (i.e., to the increased role and significance of social ways of providing security and protection from threats), primarily with the help of external law-enforcing actors such as the state, police, and army. Securitization counterweights the development of social and psychological mechanisms of security-developing motivation for safe behavior, personal self-regulation, and self-production of security as an internal feeling of protection.
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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 | 0.007 | 0.031 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.032 | 0.030 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 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".