Bibliographic record
Abstract
Defining privacy is an elusive task ( Hunt, 2015 : 161–162). 1 Daniel Solove, an authority on privacy law and theory, describes it as a concept in “disarray” ( Solove, 2008 : 1)—something that cannot “be reduced to a singular essence” ( Solove, 2013 : 24). Instead, it is a smattering of related concepts best understood “pluralistically rather than as having a unitary common denominator” ( Solove, 2008 : 9). In the digital era, this fundamental aspect, combined with the rise of information and computer technologies, has created many significant privacy conundrums. As Luciano Floridi notes, such technologies have simultaneously augmented and eroded informational privacy, posing challenges to policy approaches predicated either on regulating activity that results in undesirable consequences (the “consequences” approach) or that violates human rights or welfare (the “rights” approach) ( Floridi, 2005 : 193–194). The consequences approach has struggled to address the proposition that “a society devoid of any informational privacy may not be a better society,” while the rights approach confronts definitional issues of mixed public–private information and imprecision around foundational concepts like ownership ( Floridi, 2005 : 194). Such questions reveal that scholars might not be able to agree on what privacy is , even if they tend to know what it is about . Whether it implicates control over the collection, storage, use, or disclosure of information (or the consent to such practices by others) or whether it is about a “right to be let alone” 2 in “free zones” ( Solove, 2013 : 50) away from others’ scrutiny, interference, intrusion, or access, privacy is power. 3 With increasing calls for human-centered AI—aligning the technology to the flourishing of people and their interests—addressing issues about AI’s impact on privacy remains a pivotal question.
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 | 0.009 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.023 |
| Scholarly communication | 0.015 | 0.026 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.008 | 0.009 |
| Insufficient payload (model declined to judge) | 0.012 | 0.003 |
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".