A sociosemiotic interpretation of cybersecurity in U.S. legislative discourse
Bibliographic record
Abstract
Based on one specially created corpus of U.S. cybersecurity-related laws, this study employs the corpus approach to examine the referent objects and securitizing actors in U.S. cybersecurity legislative discourse, which are two critical issues in constructing security, including cybersecurity. Through corpus data analysis, it is found that unlike traditional security, cybersecurity has become more people-oriented in terms of referent objects with critical infrastructure as a key referent object. Additionally, the role of private sectors and cooperative security are highlighted in U.S. cybersecurity legislative discourse. From a sociosemiotic perspective, it is noted that the meaning-making process of U.S. cybersecurity not only is conveyed by the texts but also interacts with other sign systems, such as historical background, cyberspace as a virtual realm and social contexts, which suggests that the specific meanings of signs constructing cybersecurity and cybersecurity itself should be interpreted in specific temporal or spatial contexts. Furthermore, a sociosemiotic approach to U.S. cybersecurity legislative discourse also offers valuable insights to how signs and concepts in cybersecurity contribute to sketching a holistic landscape of cybersecurity and further security on a large scale.
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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 | 0.005 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.008 | 0.009 |
| Science and technology studies | 0.005 | 0.012 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.001 | 0.003 |
| 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".