Replication package for "Religion exhibits the greatest cultural diversity across 117 countries"
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Replication package for: Bentzen, J.S., Knudsen, A.S.B., Sperling, L.L., & Norenzayan, A. (2025), "Religion exhibits the greatest cultural diversity across 117 countries", Nature Communications. ----------------------------------------------------------- FILES ----------------------------------------------------------- 1_prepare_data.do – Prepares variables from the integrated EVS–WVS dataset. 2_Fig_*.do – Scripts for generating all figures (main text and SI). cntr_id.dta – Crosswalk file mapping country identifiers. readme.txt – This file. ----------------------------------------------------------- INSTRUCTIONS ----------------------------------------------------------- 1. Download the integrated European Values Study (EVS) and World Values Survey (WVS) dataset (1981–2022). Detailed instructions are available here: https://europeanvaluesstudy.eu/methodology-data-documentation/integrated-values-surveys/data-and-documentation/ Access requires free registration. 2. Save the integrated file as: Integrated_values_surveys_1981-2022.dta 3. Open Stata (version 17 or higher) and set your working directory at the top of each script (see line 5 in 1_prepare_data.do). 4. Run the scripts in order: - 1_prepare_data.do - All 2_Fig_*.do scripts (each produces one or more figures for the main text and Supplementary Information).
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it