Towards Understanding of Human Rights Researchers’ Data Analysis Practices - An Interview Study
Bibliographic record
Abstract
The impetus to assist human rights researchers in data analysis is stronger than ever. However, little is known in the literature on human rights researchers’ practices in collecting, managing, and analyzing their research data. Addressing this gap, we interviewed 13 researchers whose research areas are related to human rights issues.Aider les chercheurs en droits de l'homme dans l'analyse des données n’a jamais connu un tel engouement. Cependant, la littérature sur les pratiques des chercheurs en droits de l'homme ne contient presque rien sur la collecte, la gestion et l'analyse de leurs données de recherche. Afin de combler cette lacune, nous avons interrogé 13 chercheurs dont les aires de recherche sont liées à des questions de droits de l'homme.
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 | 0.234 | 0.327 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.005 |
| Science and technology studies | 0.013 | 0.020 |
| Scholarly communication | 0.015 | 0.017 |
| Open science | 0.003 | 0.010 |
| Research integrity | 0.004 | 0.007 |
| Insufficient payload (model declined to judge) | 0.002 | 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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".