# Convergence in Science, Divergence in Religion: Calibrated Framing Differences Across Wikipedia's Language Editions

arXiv: https://arxiv.org/abs/2608.21821 · HTML: https://mlsanity.com/paper/2608.21821/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 13 of its 19 references that are in the map.
- First posted on arXiv 22 Aug 2026.

## Builds on (most cited first, 13 of 13)

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- [Universal Sentence Representation Learning with Conditional Masked Language Model](https://mlsanity.com/paper/2012.14388/) (2020)
- [Locating Information Gaps and Narrative Inconsistencies Across Languages: A Case Study of LGBT People Portrayals on Wikipedia](https://mlsanity.com/paper/2410.04282/) (2024)
- [Linguistic neighbourhoods: explaining cultural borders on Wikipedia through multilingual co-editing activity](https://mlsanity.com/paper/1603.04225/) (2016)
- [More Women, Same Stereotypes: Unpacking the Gender Bias Paradox in Large Language Models](https://mlsanity.com/paper/2503.15904/) (2025)
- [The most controversial topics in Wikipedia: A multilingual and geographical analysis](https://mlsanity.com/paper/1305.5566/) (2013)
- [Factual Inconsistencies in Multilingual Wikipedia Tables](https://mlsanity.com/paper/2507.18406/) (2025)
- [Supervised Semantic Differential for Cross-Cultural Concept Analysis: A Case Study of Human Affect](https://mlsanity.com/paper/2605.28225/) (2026)
- [Same question, different history: language, national identity, and credit in large language models](https://mlsanity.com/paper/2606.23164/) (2026)

## Abstract (arXiv)

When Wikipedia's language editions describe the same concept, how differently do they frame it? Prior work measures coverage gaps between editions; we measure framing distance for matched concepts. We analyze 2,799 valid articles from 3,000 possible concept-language observations, spanning 150 Wikidata-anchored concepts, 20 language editions, 4 domains, and a calibration set. Raw embedding distances reflect both content differences and how well the encoder aligns each language pair. Even among calibration concepts with stable cross-cultural denotations (e.g., chemical elements, numbers, colors), the largest language-pair mean distance is 3.6 times the smallest, and distances are typically smaller within language families. We define a baseline-adjusted distance (calibrated distance): the distance between two language versions of a concept minus the mean distance for calibration concepts in the same language pair. This adjustment substantially reduces pair-specific alignment differences and the language-family pattern. Across three multilingual encoders (LaBSE, multilingual MPNet, and CMLM), scientific articles align more closely than calibration articles, and all three rank religion first and science/technology last. Concept-level rankings are highly consistent across encoders (Spearman rho=0.75-0.79 for MPNet and CMLM relative to LaBSE). Religion lies significantly above the calibration baseline under LaBSE. Within politics, divergence concentrates on concepts such as censorship and refugee, while democracy and human rights are among the most aligned. Code, data, and per-language-pair calibration baselines are released.footnotehttps://github.com/hhchen1105/cross-linqual-concept