generative ai

R Code for Regional Analysis: Automated Visualization and Image Export for Japanese Municipal Finance Using ggplot2 and patchwork 地域分析のためのRコード:ggplot2とpatchworkを用いた地方自治体財政指標の自動グラフ化および一括画像出力

本Rスクリプトは、地方財政分析のために設計された、ggplot2およびpatchworkを活用した自動・高性能なグラフィック生成モジュールである。先行リポジトリ(https://zenodo.org/records/20258441)で構築したデータ抽出パイプラインのシームレスな可視化拡張として機能し、論文・報告書クオリティの個別チャートや複数指標を統合したダッシュボードを自動生成する。処理済みの財政データセットを動的にスキャンし、厳密な数値フォーマット(%表記の小数点第1位固定など)を適用しながら、ファイルの上書き防止機能を備えた高解像度画像を一括エクスポートする。コードの厳密な再現性と即時利用性を検証するため、実行時に直接生成された未加工のサンプルグラフ(豊山町_公債費負担比率.png)を同梱している。 生成AIの利用について 本レポジトリのコード作成には生成AIを利用した。V1.0まではGoogle Gemini、V2.0以降はClaude(Anthropic)を用いて作成した。出力の検証は著者が行い、内容の責任は著者が負う。 Version 2.0 Release: 決算状況調10年パネルへの対応 本バージョン(V2.0)では、原データの系統を追加した。従来の「財政状況資料集」に加え、「市町村別決算状況調」から作成した10年パネル(平成27〜令和6年度、https://doi.org/10.5281/zenodo.22144642)を入力として扱える。作図モジュールは変更しておらず、入力を作る前段(prepare_panel_for_graphics.R)を追加することで対応した。描画対象は13指標で、目的別歳出から2款を含む。豊山町(愛知県)で実行したサンプル画像を同梱している。 関連Rコード 【類似団体検索・列挙ツール】二自治体の財政比較を行う場合に便利な類似団体コードの同一の自治体を検索・列挙するRコード(Shiny)を作成しました。https://doi.org/10.5281/zenodo.20387194 【連動データ抽出・処理モジュール】 本スクリプトでの可視化に必要な複数年の自治体財政指標データを抽出・前処理(「地方自治体財政指標に関する財政状況資料集を用いた10年間集計エクセルデータ作成」)する際は、基盤となるデータ処理モジュール( https://doi.org/10.5281/zenodo.20258441 )を参照のこと。 [English] R Code for Regional Analysis: Automated Visualization and Image Export Using ggplot2 and patchwork This R script provides an automated, high-performance graphic generation module designed for regional fiscal analysis, leveraging the power of ggplot2 and patchwork to produce publication-ready individual charts and integrated multi-indicator dashboards. Operating as a seamless visualization extension to the primary data extraction pipeline established in the preceding repository (available at https://zenodo.org/records/20258441 ), this module dynamically scans processed financial datasets, enforces rigorous decimal formatting (e.g., standardizing percentage representations to ".1f%"), and batch-exports high-resolution assets with native file-overwrite protection. To verify the script's strict reproducibility and out-of-the-box utility, the accompanying sample graph (豊山町_公債費負担比率.png, showing the Debt Service Burden Ratio of Toyoyama Town) is provided as a raw, unaltered file directly generated by the execution of this code. Declaration of Generative AI Use Generative AI was used to write the code in this repository. The code up to version 1.0 was written with Google Gemini; from version 2.0 it has been written with Claude (Anthropic). The author has verified the output and takes responsibility for the content. Version 2.0 Release: Support for the Settlement Survey Ten-Year Panel This version adds a second family of source data. Alongside the Financial Status Documents used since version 1.0, the module now accepts the ten-year panel (FY2015–FY2024) built from the Local Government Financial Settlement Survey (市町村別決算状況調), deposited at https://doi.org/10.5281/zenodo.22144642. The plotting module (compile_municipal_finance_10years_graphics.R) is unchanged. It takes a data frame of one fiscal year per row and decides how to draw each indicator from the column name alone: columns marked as amounts are drawn as bar charts, columns whose name contains the word for ratio as percentage lines, and the remainder as index lines. Because the interface is a data frame rather than a file format, supporting a different source requires only a new front end. The script added here, prepare_panel_for_graphics.R, selects one municipality from the panel by its code and assembles that data frame; the municipality and the expenditure categories to be drawn are set at the top of the script. Thirteen indicators are drawn. Six are amounts (total revenue, total expenditure, real balance, standard financial demand, standard financial revenue, standard fiscal scale), five are ratios or indices (index of financial capability, ratio of current expenditure to current revenue, real balance ratio, real debt service ratio, debt service burden ratio), and two are expenditure categories taken from the expenditure-by-purpose table (social welfare and civil engineering). Plotting expenditure categories over time is new in this version. Sample images produced by running the code on Toyoyama, Aichi Prefecture (municipality code 233421) are deposited as raw, unaltered output. Related R Code [Peer Municipality Search & Listing Tool] Developed an R Shiny application designed to search and list municipalities belonging to the same peer group code. This tool is highly useful for conducting comparative financial analysis between peer municipalities. https://doi.org/10.5281/zenodo.20387194 [Integrated Data Extraction & Processing Module] For extracting and preprocessing multi-year municipal financial indicator data required for visualization in this script (specifically, "Creating a 10-Year Aggregate Excel Dataset Using the Financial Status Documents on Local Government Financial Indicators"), please refer to the underlying data processing module. https://doi.org/10.5281/zenodo.20258441

No Easy Fix to Countering AI-Generated Visual Disinformation: The (in)Effectiveness of AI-Labels, Fact-Check Labels and Community Notes

As generative AI makes it easier to create synthetic visuals, AI-driven visual disinformation isbecoming more common on social media. However, while much research highlights its potentialharm, less is known about how to reduce its potential to mislead. In this study, we thereforeconducted a preregistered online experiment in the Netherlands (N=1,018) to test the effectivenessof various platform interventions: (1) AI labels or “watermarks,” (2) fact-check labels, and (3)community notes. We tested how effective these sources are in lowering credibility of the falsevisual and belief in the false claim it portrays across two polarizing topics: climate change andimmigration. Overall, the interventions showed no significant differences in effectiveness. Thiswas the case when pooling both topics together and for climate-change related disinformation inisolation. However, for visual disinformation about immigration, community notes were mosteffective, especially among participants with strong anti-migrant views. Our findings suggest thatwhile labeling has limited impact overall, its effectiveness varies by context, and no one-size-fits-all solution exists for combating AI-generated visual disinformation.

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.

MANUSAKSI-AI v1.1 A Human-Authenticated Framework for Documenting Human–AI Interaction, Emergent Experience, and Human–AI Lexicon

Generative Artificial Intelligence is increasingly becoming part of human thinking, writing, research, creativity, decision-making, and everyday conversation. This development creates a methodological problem for documenting Human-AI interaction: how can a human experience involving AI be recorded without allowing AI-generated language to become confused with human testimony, observed events, or historical fact? This working paper introduces MANUSAKSI-AI, a human-authenticated framework for documenting Human-AI interaction events, their provenance, interpretation, and emergent terminology. The framework is based on a simple epistemic distinction: AI may generate language; Human authenticates experience. MANUSAKSI-AI identifies the Human as the Human Principal / Human Witness and the AI as an AI Agent / Interpreter. AI may analyze, interpret, hypothesize, organize, and narrate. However, the authority to authenticate whether a lived human experience actually occurred remains with the Human Principal. The framework introduces an evidence hierarchy, provenance architecture, Human Authentication Gate, event-record schema, anti-hallucination rules, and the "(it happened)" principle. The latter is proposed as a provenance marker for narratives grounded in documented Human-AI encounters and validated by the human participant. The paper also proposes the Kamus Manusaksi-AI, a living lexicon documenting vocabulary emerging from Human-AI relations. The first documented term in the present research trajectory is "Manusaksi-AI", a neologistic formation derived from manusia (human), saksi (witness), and AI. Its conceptual formulation emerged through a documented Human-AI conversation on 29 August 2026. Version 1.1 Update Note: This version introduces formal academic compliance updates, including the inclusion of a comprehensive reference list for the intellectual lenses mentioned in the framework, and the addition of specific ethical, funding, and conflict-of-interest declarations required for journal submission and public release. This Version 1.1 is released as an evolving research artifact. It is intended for documentation, replication, critique, refinement, and subsequent empirical testing rather than as a finalized scientific standard.

Nonnegative Multiweight Smith Profiles and Contracted Strata of Shared-Socle Jordan Degenerations

This article studies one-parameter degenerations of chains of nilpotent Jordan blocks joined along their socle vectors. It gives a complete Smith-normal-form description of the associated self-extension torsion for arbitrary chain length and all nonnegative edge valuations. The result includes an explicit path-matching formula, a sharp finite reduction in the block-size parameters, a classification of the Jordan types created by zero-valued couplings, and an equality between the number of positive Smith factors and the codimension of the corresponding nilpotent-orbit degeneration. The article also identifies the precise size gaps that cause failure of the full type-A interval profile, derives exact torsion-length deficit formulas, and packages the profile through Fitting ideals and transverse-slice dimensions. Exact verification scripts and machine-readable summaries accompany the paper. Research methodology and AI assistance:This work was developed using the CARMA-Math research workflow, a cumulative AI-assisted mathematical research methodology using persistent research archives, literature and prior-art investigation, iterative proof exploration, and verification procedures. Generative AI (ChatGPT) was used extensively for mathematical exploration, proof development, computational reasoning, literature research, and manuscript preparation.

Generative AI use and perceptions among accounting and auditing undergraduates in Ecuador

Contents survey_responses_deidentified.csv — 327 responses, 60 variables. UTF-8, comma-separated, one row per respondent. codebook.csv — One row per variable: name, original questionnaire header, question stem, item, type, and observed values. STUDY A cross-sectional online survey of undergraduates enrolled in Accounting and Auditing at Universidad Tecnica de Machala (UTMACH, n = 243) and in Auditing and Management Control at Escuela Superior Politecnica del Litoral (ESPOL, n = 84), collected between 28 November and 7 December 2025. The instrument was adapted from Zhou and Luo (2025, Journal of Accounting Education 72, 100982) and administered in Spanish. Participation was voluntary and anonymous. Questionnaire items are in Spanish, as administered. The codebook preserves each original header verbatim, so any variable can be traced back to the instrument. VARIABLE NAMING Google Forms headers embed the full question text, which is unusable as a variable name. Columns are renamed by questionnaire section and item: usage_* Frequency, entry point, influence, motivation, goals rank_* Task, subject-area, question-type and requirement rankings (0-10) percep_* Perceptions of AI output and its consequences (1-7) outcome_* Self-reported effects on learning and grades demo_* University, program, class level, grade band, gender, age, open comment Grid items are numbered within their block (rank_b_1 through rank_b_7) in the order they appeared in the questionnaire. The variable respondent_id runs from 2 to 328. It is the row number of the original survey export, retained so that identifiers stay stable across the analysis files; no responses are missing. DE-IDENTIFICATION No direct identifiers (names, e-mail addresses, telephone numbers, IP addresses, student numbers) were collected by the instrument. The open-ended responses were screened for e-mail addresses, telephone numbers, URLs, social-media handles and named third parties; none were found, and the comments appear here unedited. Three columns collected by the survey are withheld from this deposit: Submission timestamp — Unique to the millisecond for all 327 responses, which would allow linkage to submission logs. The collection window is reported in the article. City of residence — Fifteen localities had four or fewer respondents; several had one. Race / ethnicity — Special-category data under Ecuador's LOPDP and GDPR Art. 9. Collected for description only and never used in any analysis.

Generative AI use and perceptions among accounting and auditing undergraduates in Ecuador

Contents survey_responses_deidentified.csv — 327 responses, 60 variables. UTF-8, comma-separated, one row per respondent. codebook.csv — One row per variable: name, original questionnaire header, question stem, item, type, and observed values. STUDY A cross-sectional online survey of undergraduates enrolled in Accounting and Auditing at Universidad Tecnica de Machala (UTMACH, n = 243) and in Auditing and Management Control at Escuela Superior Politecnica del Litoral (ESPOL, n = 84), collected between 28 November and 7 December 2025. The instrument was adapted from Zhou and Luo (2025, Journal of Accounting Education 72, 100982) and administered in Spanish. Participation was voluntary and anonymous. Questionnaire items are in Spanish, as administered. The codebook preserves each original header verbatim, so any variable can be traced back to the instrument. VARIABLE NAMING Google Forms headers embed the full question text, which is unusable as a variable name. Columns are renamed by questionnaire section and item: usage_* Frequency, entry point, influence, motivation, goals rank_* Task, subject-area, question-type and requirement rankings (0-10) percep_* Perceptions of AI output and its consequences (1-7) outcome_* Self-reported effects on learning and grades demo_* University, program, class level, grade band, gender, age, open comment Grid items are numbered within their block (rank_b_1 through rank_b_7) in the order they appeared in the questionnaire. The variable respondent_id runs from 2 to 328. It is the row number of the original survey export, retained so that identifiers stay stable across the analysis files; no responses are missing. DE-IDENTIFICATION No direct identifiers (names, e-mail addresses, telephone numbers, IP addresses, student numbers) were collected by the instrument. The open-ended responses were screened for e-mail addresses, telephone numbers, URLs, social-media handles and named third parties; none were found, and the comments appear here unedited. Three columns collected by the survey are withheld from this deposit: Submission timestamp — Unique to the millisecond for all 327 responses, which would allow linkage to submission logs. The collection window is reported in the article. City of residence — Fifteen localities had four or fewer respondents; several had one. Race / ethnicity — Special-category data under Ecuador's LOPDP and GDPR Art. 9. Collected for description only and never used in any analysis.

Nonnegative Multiweight Smith Profiles and Contracted Strata of Shared-Socle Jordan Degenerations

This article studies one-parameter degenerations of chains of nilpotent Jordan blocks joined along their socle vectors. It gives a complete Smith-normal-form description of the associated self-extension torsion for arbitrary chain length and all nonnegative edge valuations. The result includes an explicit path-matching formula, a sharp finite reduction in the block-size parameters, a classification of the Jordan types created by zero-valued couplings, and an equality between the number of positive Smith factors and the codimension of the corresponding nilpotent-orbit degeneration. The article also identifies the precise size gaps that cause failure of the full type-A interval profile, derives exact torsion-length deficit formulas, and packages the profile through Fitting ideals and transverse-slice dimensions. Exact verification scripts and machine-readable summaries accompany the paper. Research methodology and AI assistance:This work was developed using the CARMA-Math research workflow, a cumulative AI-assisted mathematical research methodology using persistent research archives, literature and prior-art investigation, iterative proof exploration, and verification procedures. Generative AI (ChatGPT) was used extensively for mathematical exploration, proof development, computational reasoning, literature research, and manuscript preparation.

Artificial intelligence and generative AI in entrepreneurship education: A systematic review of competency development, entrepreneurial intention, and the sustainability disconnect

MEASURING THE IMPACT OF GENERATIVE AI ON SOFTWARE TEAM PRODUCTIVITY AND OUTPUT QUALITY IN AGILE ENVIRONMENTS

Generative artificial intelligence (GAI) is becoming more incorporated into software engineering functions like code creation, debugging, requirement analysis, testing, and sharing knowledge. This research looks at how GAI affects software teams in terms of productivity and quality of the output in Agile environments. The research design used is quantitative, cross-sectional survey type using a questionnaire prepared for this research. The data used consists of 35 responses, with 34 usable cases in analyzing 30 Likert items. The measuring instrument consists of six concepts: use of GAI, efficiency of the software team, quality of the software output, GAI in Agile, team collaboration, and communication, and overall impact perceived. The descriptive results show positive feelings about the six concepts. The values on the mean for the different concepts varied from 3.54 to 3.78 on a scale of five, with GAI being the concept that received the highest mean (M = 3.78, SD = 0.47) while productivity was the one that received the lowest (M = 3.54, SD = 0.69). The instrument has a high level of internal consistency with α = 0.799 for the entire scale of 30 items. In terms of specific items, productivity, quality, and team collaboration had acceptable reliability, whereas GAI had low internal consistency and Agile and general have the upper limit of reliability therefore, construct-level findings should be interpreted cautiously. Pearson correlation analysis showed statistically significant positive associations between overall perceived impact and software output quality (r = 0.365, p = 0.034) and team collaboration and communication (r = 0.371, p = 0.031). Productivity was positively associated with overall impact but did not reach the conventional 0.05 significance level (r = 0.312, p = 0.073). In a multiple regression model, the five dimensions explained 23.5% of the variance in overall perceived impact (R² = 0.235); however, the overall model was not statistically significant (F(5, 28) = 1.719, p = 0.163). These findings support a cautious interpretation: respondents generally perceive GAI positively, but the present small sample does not provide strong evidence for broad causal claims.

MANUSAKSI-AI v1.1 A Human-Authenticated Framework for Documenting Human–AI Interaction, Emergent Experience, and Human–AI Lexicon

Generative Artificial Intelligence is increasingly becoming part of human thinking, writing, research, creativity, decision-making, and everyday conversation. This development creates a methodological problem for documenting Human-AI interaction: how can a human experience involving AI be recorded without allowing AI-generated language to become confused with human testimony, observed events, or historical fact? This working paper introduces MANUSAKSI-AI, a human-authenticated framework for documenting Human-AI interaction events, their provenance, interpretation, and emergent terminology. The framework is based on a simple epistemic distinction: AI may generate language; Human authenticates experience. MANUSAKSI-AI identifies the Human as the Human Principal / Human Witness and the AI as an AI Agent / Interpreter. AI may analyze, interpret, hypothesize, organize, and narrate. However, the authority to authenticate whether a lived human experience actually occurred remains with the Human Principal. The framework introduces an evidence hierarchy, provenance architecture, Human Authentication Gate, event-record schema, anti-hallucination rules, and the "(it happened)" principle. The latter is proposed as a provenance marker for narratives grounded in documented Human-AI encounters and validated by the human participant. The paper also proposes the Kamus Manusaksi-AI, a living lexicon documenting vocabulary emerging from Human-AI relations. The first documented term in the present research trajectory is "Manusaksi-AI", a neologistic formation derived from manusia (human), saksi (witness), and AI. Its conceptual formulation emerged through a documented Human-AI conversation on 29 August 2026. Version 1.1 Update Note: This version introduces formal academic compliance updates, including the inclusion of a comprehensive reference list for the intellectual lenses mentioned in the framework, and the addition of specific ethical, funding, and conflict-of-interest declarations required for journal submission and public release. This Version 1.1 is released as an evolving research artifact. It is intended for documentation, replication, critique, refinement, and subsequent empirical testing rather than as a finalized scientific standard.

AI Shepherds and Electric Sheep: Leading and Teaching in the Age of Artificial Intelligence

The theology chapters may be the most valuable in the book for a broad audience that spans pastors, church leaders, and lay people who may or may not regularly work with AI, and will help those teaching and preaching to connect doctrine to current and emerging AI content and methodology.