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アフリカ選挙言説における気候変動：南アフリカ（2024年）とナイジェリア（2023年）選挙のデータと再現ファイル

Climate Change in African Electoral Discourse: Data and Replication Files for South Africa (2024) and Nigeria (2023) Elections (原題)

Dominic Ayegba Okoliko, Mehita Iqani, Mehita Iqani

Stellenbosch University (SUN)データセット2026-08-05#AI×ESGOrigin: Global
DOI: 10.25413/sun.30747584
原典: https://doi.org/10.25413/sun.30747584

🤖 gxceed AI 要約

日本語

南アフリカ2024年国政選挙とナイジェリア2023年大統領選挙の選挙言説における気候変動の顕在性を、BERTopicモデリングと課題顕在性・競合枠組みを用いて分析する多段階プロジェクトの補足資料を提供する。生データ、処理スクリプト、分析コード、出力を含むオープンなリポジトリで、独立した検証と再現を可能にする。第1・2段階は完了し、第3段階の横断比較研究が予定されている。

English

This living repository provides supplementary materials for a multi-phase project analyzing the salience of climate change in electoral discourse in South Africa's 2024 national election and Nigeria's 2023 presidential election, using BERTopic modeling and issue-salience/competition frameworks. It includes raw data, processing scripts, analytical code, and outputs for independent verification and replication. Phases 1 and 2 are complete, with a forthcoming cross-country comparative study.

Unofficial AI-generated summary based on the public title and abstract. Not an official translation.

📝 gxceed 編集解説 — Why this matters

日本のGX文脈において

日本のGX文脈では、気候変動が選挙や政策アジェンダでどのように扱われるかは、SSBJ開示や投資家対応と間接的に関連する。日本では気候変動が政策課題としての優先度が高く、選挙言説の分析手法は日本の政策コミュニケーションやステークホルダー対応に示唆を与える可能性がある。ただし、直接的な実務関連性は限定的。

In the global GX context

Globally, this work contributes to understanding climate change salience in democratic processes, particularly in African contexts. It offers a methodological template for analyzing climate discourse in elections, relevant to climate policy communication and public engagement. The open data and replication files support transparency and further research in climate politics.

👥 読者別の含意

🔬研究者:Provides a replicable BERTopic-based methodology for analyzing climate issue salience in electoral discourse, with open data for further comparative studies.

🏛政策担当者:Highlights how climate change competes with other issues in electoral agendas, informing climate communication and policy prioritization strategies.

📄 Abstract（原文）

This living repository contains supplementary materials for a multi-phase project investigating the salience of climate change in electoral discourse across African democracies, focusing on South Africa's 2024 national election and Nigeria's 2023 presidential election. The project examines how climate change competes for attention with other policy issues during national elections, using BERTopic modelling combined with issue-salience and issue-competition frameworks.Open Science CommitmentAll raw data, processing scripts, analytical code, and outputs are included to enable independent verification and replication. Full repository structure, folder-by-folder contents, and technical specifications are documented in the accompanying README file.Project StructurePhase 1 (completed): South Africa's 2024 national electionPhase 2 (completed): Nigeria's 2023 presidential electionPhase 3 (forthcoming): Cross-country comparative analysisCurrent Status (Version 2.0 – July 2026)Complete materials for Phases 1 and 2 are included, supporting two stand-alone empirical papers and providing the analytical foundation for the forthcoming comparative study:South Africa: Crowded Out: Climate Change in South Africa's 2024 National Election Discourse (under review, African Journalism Studies)Nigeria: The Missing Climate: Issue Competition and Structural Invisibility in Nigeria's 2023 Electoral Agenda (published in Environmental Research Communications)Repository ContentsEach country package (SA_Data_&_Research_Files.zip, NG_Data_&_Research_Files.zip) follows the same six-stage workflow — raw data and inputs, BERTopic modelling code, model outputs, salience-computation code, salience scores, and analysis/visualisation outputs. See the README for the complete file inventory, folder structure, and technical specifications (software, embedding model, analytical framework).

🔗 Provenance — このレコードを発見したソース

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gxceed は公開メタデータに基づく研究支援データセットです。要約・翻訳・解説は AI 支援で生成されています。 最終的な解釈・検証は利用者が原典資料に基づいて行うことを前提とします。