Legitimate Invasions: What Ontario can Learn from the History of the Consumer Reporting Act
Bibliographic record
Abstract
The growth of modern surveillance has attracted great public and scholarly interest. As Justice Abella recently noted in Douez v. Facebook, the Internet has transformed the potential harms flowing from an unjustified invasion of one’s personal information. Most analyses of the associated risks, however, imply that the techniques and motivations for surveillance are new. In fact, tactics for collecting and exchanging information about individuals to gain power over those individuals are well documented since time immemorial. From William the Conquerer’s Domesday Book to IBM’s first census tabulating machine, the advantage gained through data sharing has greatly benefited the state. The history of surveillance, however, is not solely a history of government surveillance. The explosion of commercial and consumer credit, the ‘‘vital air of the system of commerce,” in the 19th century transformed surveillance by perfecting the process of flattening an individual’s identity into a monetizable reputation. Collecting and exchanging personal information became the artillery of the private sector, a necessity for growth and market saturation. Today, we are deeply accustomed to having our identities tested and our personal stories collected in commercial settings, taking for granted the infrastructures that trade them, and their justifications for doing so. In our highly mediated, digital economy, there is often no alternative to these legitimate invasions.
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 | 0.012 | 0.028 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.012 | 0.052 |
| Scholarly communication | 0.015 | 0.030 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.015 | 0.019 |
| Insufficient payload (model declined to judge) | 0.009 | 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".