Cloud Computing and Privacy Risks in the Information/Knowledge/Digital Risk Society and Economy: An Overview
Bibliographic record
Abstract
Cloud computing has revolutionised the way in which computing services are delivered and managed in the contemporary society and economy. The emergence of computers and the internet, the one hand, accelerated the swift technological developments in especially in the computing domain thus speeding up the rapid growth and diffusion of cloud computing. But, at one and the same time, on the other hand, they tectonically transformed the contemporary society and economy into information/knowledge/ digital society and economy. Both are reciprocally and interactively related, strengthening each other in their operational and functional practices. These practices, in the wake of coming of 'data revolution' and consequent 'datafication' of the society and economy, abundantly exhibited different types of security issues, especially privacy risks, which transmuted the erstwhile society and economy into an the information/knowledge/ digital risk society and economy and, simultaneously, became an hindrance to the diffusion of cloud computing, which itself is embedded in this risk society and economy in the global information capitalist order. Risks, particularly privacy risks, constitute the strong bridge and link between them. The present paper critically analyses and surveys these stated socio-technical developments.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.005 | 0.008 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".