Cloud Computing and Privacy Risks in the Information/Knowledge/Digital Risk Society and Economy: An Overview
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it