Search Engines, Personal Information and the Problem of Privacy in Public
Bibliographic record
Abstract
The purpose of this paper is to show how certain uses of search-engine technology raise concerns for personal privacy. In particular, we examine some privacy implications involving the use of search engines to acquire information about persons. We consider both a hypothetical scenario and an actual case in which one or more search engines are used to find information about an individual. In analyzing these two cases, we note that both illustrate an existing problem that has been exacerbated by the use of search engines and the Internet – viz., the problem of articulating key distinctions involving the public vs. private aspects of personal information. We then draw a distinction between “public personal information” (or PPI) and “nonpublic personal information” (or NPI) to see how this scheme can be applied to a problem of protecting some forms of personal information that are now easily manipulated by computers and search engines – a concern that, following Helen Nissenbaum (1998, 2004), we describe as the problem of privacy in 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.
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 | 0.029 | 0.046 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.007 | 0.039 |
| Scholarly communication | 0.015 | 0.027 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.014 | 0.010 |
| 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".