Measuring Human Rights Abuse from Access to Information Requests
Bibliographic record
Abstract
Existing measures of human rights abuses are often only available at the country-year level. Several more fine-grained measures exhibit spatio-temporal inaccuracies or reporting biases due to the primary sources upon which they rely. To address these challenges, and to increase the diversity of available human rights measures more generally, this study provides the first quantitative effort to measure human rights abuses from textual records of citizen-government interactions. Using a dataset encompassing over 1.5 million access-to-information (ATI) requests made to the Mexican federal government from June 2003 onward, supervised classification is used to identify the subset of these requests that pertain to human rights abuses of various types. The results from this supervised machine learning exercise are validated against (i) gold standard ATI requests pertaining to past human rights abuses in Mexico and (ii) several accepted external measures of sub-national and sub-annual human rights abuses. In doing so, we demonstrate that the measurement of human rights abuses from citizen-submitted ATI request texts can provide measures of human rights abuse that exhibit both high validity and notable spatio-temporal specificity, relative to existent human rights datasets and variables.
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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 | 0.002 | 0.024 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".