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
Although most Canadians are familiar with surveillance cameras and airport security, relatively few are aware of the extent to which the potential for surveillance is now embedded in virtually every aspect of our lives. We cannot walk down a city street, register for a class, pay with a credit card, hop on an airplane, or make a telephone call without data being captured and processed. Where does such information go? Who makes use of it, and for what purpose? Is the loss of control over our personal information merely the price we pay for using social media and other forms of electronic communication, or should we be wary of systems that make us visible—and thus vulnerable—to others as never before? \n \nThe work of a multidisciplinary research team, Transparent Lives explains why and how surveillance is expanding—mostly unchecked—into every facet of our lives. Through an investigation of the major ways in which both government and private sector organizations gather, monitor, analyze, and share information about ordinary citizens, the volume identifies nine key trends in the processing of personal data that together raise urgent questions of privacy and social justice. Intended not only to inform but to make a difference, the volume is deliberately aimed at a broad audience, including legislators and policymakers, journalists, civil liberties groups, educators, and, above all, the reading public.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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