Do You Mind NSA Affair?Does the Global Surveillance Disclosure Impact Our Students?
Bibliographic record
Abstract
In summer 2013 the discussion about security and Internet peaked when the ex-NSA man Edward Snowden uncovered secret details about his former employer NSA. Since then bad news and stories about government surveillance have come along daily worldwide. But did they change anything according to our way of working and living with the Internet? We wanted to know, whether there is a change of Internet behavior to be determined among freshmen coming to Graz University of Technology (TU Graz). On base of an annual questionnaire that is carried out by the Department of Social Learning at TU Graz since 2007 we found out that although a quarter of polled students are influenced by those disclosures there is no decrease in Internet usage to be realized compared to former years results; quite the reverse: applications working on cloud- principle like Dropbox are strongly upcoming. Apart from that, the long term survey generally mirrors new media competences and Internet usage of TU Graz freshmen. This paper discusses this years results and progressions of the survey. It targets the disclosure aspect under the special focus of cloud-applications.
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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 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.011 | 0.002 |
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".