The Scoop: How to Protect Your Digital Privacy in the Age of Surveillance Capitalism
Bibliographic record
Abstract
The perception of Generation Y as “digital natives”, whose very minds have been shaped by the technology they were raised alongside (Prensky, 2001), is increasingly under fire. Many young adults struggle with anything more than basic operation of technology, and lack the digital literacy to find and discern accurate information on the Internet. In an environment of increasing surveillance and malicious intrusion into corporate and personal data, there can be severe consequences to this skill gap. However, because of the assumption of competence attached to Generation Y, accessible post-educational resources are scarce for those outside of specific technology-related fields. This project aims to create a game which will serve as a stepping-stone for those interested in gaining knowledge and empowering themselves to take control of their digital lives. By presenting real-world scenarios of gradually increasing complexity, through gameplay environments focused on strategy and careful decision-making, digital literacy and security skills can be built from the ground up to serve as a foundation for further research and the development of secure personal habits.
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.
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 | 0.006 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.010 | 0.023 |
| Scholarly communication | 0.009 | 0.014 |
| Open science | 0.001 | 0.009 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.004 | 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".