Information Brokers, Fairness,\nand Privacy in Publicly Accessible\nInformation
Bibliographic record
Abstract
The European Union, Canada, and the United States have each grappled with what\ncounts as fair business practices in relation to information services that collect and\npackage personal information that has ended up in one way or another online. On\nthe open internet, this personal information often originates from two types of online\nsources: public records like arrests, mugshots, court decisions, and bankruptcy records;\nand user-generated content hosted on social media platforms and sites. This article\nargues that personal information that has been exposed to public view — be it by a\ngovernment institution, another individual or organization, or by the data subject him\nor herself — should not be considered fair game to any and all subsequent commercial\nexploitation. The blunt concept of “public” information should be refined to a more\nnuanced understanding of “publicly accessible” information, where public access can be\nlimited to particular purposes. By focusing on fairness in business dealings in publicly\naccessible personal information, it should be possible to move beyond a fixation on\nlocating the elusive divide between private and public online information, and instead\nframe privacy as situated in a three-way balance of interests between the business, the\npublic, and the data subject.
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How this classification was reachedexpand
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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.008 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.013 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".