Prediction, Preemption, Presumption: How Big Data Threatens Big Picture Privacy
Bibliographic record
Abstract
Big Data has become a familiar concept in legal and social scientific literature and debate. This paper explores the nature of Big Data by examining its intersection with consequential, preferential, and preemptive predictions. The authors address how fundamental jurisprudential principles, including the presumption of innocence and the associated privacy and due process values are threatened by an overreliance on Big Data and the way it is put to use in making preemptive predictions. While the authors acknowledge the benefits of big data, they question whether the trade-off is worth it in light of the resultant undesirable social consequences. Ultimately, the employment of Big Data by corporations, governmental entities, and individuals can replace proof with mere prediction. In order to mitigate potential negative outcomes, the authors maintain that subjects of preemptive predictions must be able to scrutinize and contest projections and assumptions about themselves.
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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 | 0.042 | 0.073 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.009 | 0.066 |
| Scholarly communication | 0.015 | 0.027 |
| Open science | 0.003 | 0.011 |
| Research integrity | 0.012 | 0.021 |
| 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".