Teaching the Value of Privacy in an Age of Pervasive Surveillance Technology
Bibliographic record
Abstract
In our age of social media and ever-present recording devices it is increasingly common to hear that privacy is dead, and that we ought to simply adjust to this new reality. In particular, for students today raised in a world inundated with technology, they are often simply unaware of even the value of privacy. Emerging technologies currently in development, such as Google Glass, threaten to erode privacy even further. What often drives resignation in the privacy debate concerning new technology is a zero-sum notion that privacy and a certain class of new technologies are simply incompatible. This, however, is a mistaken notion. Current work being done by the Office of the Information and Privacy Commissioner of Ontario can serve as a model for how this zero-sum model can be countered. Their Privacy by Design initiative seeks to embed new government surveillance technologies with privacy-protecting mechanisms. Such initiatives give hope that solutions could be developed in order to address privacy concerns related to commercially available technology as well. But unless an immediate effort is made to teach the value of privacy in our curriculum, there will be little will among young people today to reach for solutions that will protect privacy in the development and adoption of new technologies. The result will instead be a new reality where privacy really is vanquished, and the feeling of being constantly monitored is pervasive.
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 | 0.015 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.010 | 0.037 |
| Scholarly communication | 0.014 | 0.027 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.007 | 0.020 |
| 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".