Replication package for "Religion exhibits the greatest cultural diversity across 117 countries"
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.
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 | 0.017 | 0.135 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.004 |
| Bibliometrics | 0.004 | 0.007 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.451 | 0.169 |
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".