Bibliographic record
Abstract
International human rights efforts have been overly reliant on reactive tools and focused on treaty compliance, while often under emphasizing the prevention of human rights violations. I argue that data analytics can play an important role in refocusing the international human rights regime on its original goal of preventing human rights abuses, but it comes at a cost. There are risks in advancing a data-driven approach to human rights, including the privileging of certain rights subject to quantitative measurement and the precipitation of further human rights abuses in the process of preventing other violations. Moreover, the increasing use of big data can ultimately privatize the international human rights regime by transforming the corporation into a primary gatekeeper of rights protection. Such unintended consequences need to be addressed in order to maximize the benefits and minimize the risks of using big data in this field.
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 | 0.059 | 0.118 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.006 | 0.036 |
| Scholarly communication | 0.023 | 0.044 |
| Open science | 0.004 | 0.016 |
| Research integrity | 0.022 | 0.023 |
| Insufficient payload (model declined to judge) | 0.016 | 0.005 |
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".