Replication Data for: The Global Resonance of Human Rights: What Google Trends Can Tell Us
Bibliographic record
Abstract
Project files for an article titled: "The Search for Human Rights: A Global Analysis Using Google Data", conditionally accepted at American Political Science Review. The files in this reproduction archive exist here: https://github.com/CJFariss/Human-Rights-Search; and a static version exists at the APSR dataverse archive here: https://dataverse.harvard.edu/dataverse/the_review. Article Abstract: Where is the human rights discourse most resonant? We use aggregated cross-national Google search data to test two divergent accounts of why human rights appeal to some populations but not others. The top-down model predicts that nationwide interest in human rights is attributable mainly to external factors like foreign direct investment, transnational NGO campaigns, or international legalization, where the bottom-up model highlights the importance of internal factors like economic growth and persistent repression. We find more evidence for the latter model: not only is interest in human rights more concentrated in the Global South, the discourse is most resonant where people face regular violence at the hands of their home government. In drawing these inferences, this article confronts high-level debates over whether human rights will remain relevant in the future, and whether the discourse still animates counter-hegemonic modes of resistance. These answer to both questions, our research suggests, is “yes.”
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 | 0.003 | 0.033 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.014 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.127 | 0.100 |
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".