Version 1.3.0

False Authorship: Methods and materials package

View Dataset
Spinellis, Diomidis

Description

This package contains Python, shell, awk scripts, and data used to obtain the curated table and excerpt associated with the above named article.Data ContentsThe following data files are included. * README.md: This file * article-details.xlsx: Curated table with details of published articles in Microsoft Excel file format * index.html: HTML document with * links to GIJIR materials saved in the Internet Archive * a list of all the GIJIR articles’ citation data according to Crossref and links to each article’s locally available landing page, full-text PDF, plus links to Crossref metadata and the article via DOI and original journal URL. (Note that non-local, non-archived links may rot over time.) * ybs-works.json: Results of Crossref query to obtain all the publisher’s works made on 2024-09-22 * ChatGPT: Prompts and responses associated with the generation of a fake article in one of the journal’s topics. * global-us/metadata/: Article metadata as HTML files collected on 2024-09-10 * global-us/global-us.mellbaou.com/index.php/global/article/download/: A copy of the journal’s article PDFs as crawled on 2024-09-10 * spinellis business - Google Scholar.pdf: Printout of a Google Search query for the terms spinellis business made on 2025-02-06.Executable ContentsThe following programs and scripts are used to obtain the above contents.Makefile: Commands that orchestrate the articles’ analysisget-metadata.sh: Obtain article metadata pages from the journal’s web siteapply-to-pdfs.sh: Apply the specified Python script to all article PDFsextract-citations-emails.py: Extract number of probable in-text citations and corresponding author email from article PDFextract-doi-affiliations.py: Extract article DOI and affiliations from an article’s metadataextract-all-doi-affiliations.sh: Extract article DOI and affiliations from all articles’ metadataemails-to-csv.awk: Convert emails and article numbers to CSV with URL for sending emails

Citations (1)

Mentions (0)

Metrics

Dataset Index

2.2

FAIR Score

77%

Citations

1

Mentions

0

Metrics Over Time

Publication Details

DOI

Publisher

Zenodo

Assigned Domain

Subfield

Sociology and Political Science

Field

Social Sciences

Domain

Social Sciences

Confidence Score

41%

Source

Scholar Data Model

Keywords

generative AIpredatory journals

Normalization Factors

FT

13.46

CTw

1.00

MTw

1.00