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AI Impact on the Algorithmic Manufacturing of the Gender Fracture

Aug 2026 · Zenodo (CERN European Organization for Nuclear Research)

Abstract

This white paper examines the widening ideological divide between young men and young women in Generation Z, arguing that it cannot be understood solely as an organic political or sociological development. It analyzes how attention-maximizing platforms, recommendation systems, generative AI and commercialized digital intimacy can exploit mating anxieties, sexual gratification and tribal defence mechanisms, directing users into increasingly adversarial informational and relational environments. Drawing on behavioural economics, cognitive neuroscience, game theory and legal analysis, the paper traces a cross-platform pipeline extending from algorithmic rage bait and ideological priming to AI-mediated intimacy, companion systems and automated customer-relationship infrastructures. It models competition for finite human attention as a non-cooperative game in which polarizing, high-variance content becomes a commercially stable strategy—even without an explicit intention to radicalize. The paper also examines the limitations of the EU AI Act, product-liability doctrines and content-focused interventions when harm arises from optimization incentives rather than an identifiable prohibited purpose. It concludes with a strategic framework for changing those incentives, strengthening algorithmic product-liability audits and rebuilding physical, non-digitized spaces for human interaction. Its central contention is that AI does not merely reflect the generational gender fracture: under the prevailing incentives of the attention economy, it can participate in manufacturing and accelerating it.

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#generative ai Open access Sep 2026

The socio-ecological costs of AI: Toward socially responsible and sustainable communication practices

The adoption of generative artificial intelligence among communication practitioners and researchers surged after the launch of ChatGPT in November 2022, urging practitioners to critically engage in exploring pathways for fostering socially responsible and environmentally sustainable AI practices.

Emma Christensen · 4 citations · ⚡1
#generative ai Open access Aug 2026

Ten-Year Panel of Japanese Municipal Finance from the Local Government Financial Settlement Survey

This R script (make_kessan10_csv.R) converts the Local Government Financial Settlement Survey (市町村別決算状況調), published by the Ministry of Internal Affairs and Communications on its annual pages of local government financial status survey materials, into machine-readable CSV. The source workbooks are print-oriented Excel files with multi-row merged headers, issued as four separate files per fiscal year (overview and expenditure, for cities and for towns and villages). The script consolidates them into long-format panels carrying fiscal year and municipality type as columns, and also writes one file per fiscal year. The output of a run over ten fiscal years (FY2015–FY2024) is deposited alongside it: all 1,741 municipalities, with 33 overview indicators and 94 expenditure items classified by purpose, giving panels of 17,410 rows each. Every municipality and every year is checked for internal consistency: the components of each expenditure category sum to that category's total, and the sum of all categories matches the total expenditure reported in the overview table. All checks passed for all ten years. Amounts are in thousands of yen, as published; blank cells are left blank rather than filled with zero. The column structure of the source data does not change over the period covered. One definitional change affects the adjusted ratio of current expenditure to current revenue: for FY2020 and FY2021 the special bonds issued for deferred tax collection are removed from current general revenue as well. Four changes of municipality occurred: Tomiya and Nakagawa became cities in FY2016 and FY2018 respectively, each receiving a new municipality code; Sasayama was renamed Tamba-Sasayama in FY2019, and Aogashima was renamed in FY2018 in the written form of its name only, both keeping their codes. The code was written with generative AI: Claude (Anthropic) was used to write and revise it. The author has verified the output and takes responsibility for the content. Version 1.1 corrects the reading of the census population change column in the overview table, where a small negative rate written with the triangle sign used in Japanese official statistics was left blank instead of being read as a number. 56 cells across the ten years were affected; no other value changed.

Yasutoshi Moteki · 1 citation

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