Bibliographic record
Abstract
Big data is transforming the way governments provide security to, and justice for, their citizens.But it also has the potential to increase surveillance and government power.Indeed, information gathered from license plate recognition, mobile phone usage, biometric matches of DNA, facial recognition, financial transactions, and internet search history is increasingly allowing government agencies to search and cross-reference.The opportunity for big data searches then raises the question: what is the probative value of the information that results?The scientific method begins with the development of a hypothesis that is then tested against data that will either support or refute the hypothesis.That method is essentially followed in a conventional criminal investigation in which, after a suspect is first identified, evidence is gathered to either build a case against, or rule out, that suspect.The analysis of big data, by contrast, may at times be more akin to trawling for data first, only to subsequently define a hypothesis.In this paper, we investigate the conditions in which this approach may lead to problematic outcomes, including higher rates of false positives.We then sketch a big data analysis legal/policy framework that may address these problems.
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 | 0.034 | 0.093 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.003 | 0.020 |
| Scholarly communication | 0.013 | 0.027 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.005 | 0.013 |
| Insufficient payload (model declined to judge) | 0.005 | 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".