Scientific Integrity in Artificial Intelligence: Evidence from a Horizon Europe Research Project
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2026
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| _version_ | 1866901557017051136 |
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| author | Benítez Baleato, Jesús Manuel |
| author_facet | Benítez Baleato, Jesús Manuel |
| contents | <p>Tables from a corpus-level risk-classification analysis of the publications of an EU-funded research programme on artificial intelligence in the political-communication domain. The classification scheme — four axes (topic, venue, geography, disciplinarity), per-axis operationalisation, and risk-gradient thresholds — is documented in the companion framework documentation (see relatedIdentifiers).</p><p>In order to ensure the analysis stays at institutional level (avoiding personal attribution to researchers), the tables refer to individual publications by an opaque identifier (<code>pub_uid</code>, P001–P0NN) and contain no bibliographic information (titles, author names, venue names, DOIs). Aggregations report counts and percentages across the 75-publication corpus.</p><p>Contents (in <code>derived-tables/</code>): a per-publication enriched table (year, venue type, data source, classification levels, risk score) plus 27 aggregation tables along axes and cross-tabulations (per-axis distributions; risk by venue, by year, by discipline; channel-mismatch; ethics-authorisation and DPIA counts; year-trend; country and topic risk; LLM use; data-source and output-category breakdowns).</p><p>A subsequent version (2.0.0) will add the R analysis scripts, the rendered summary tables, the figures, and the summary statistics that consume these tables to reproduce the empirical reporting of the corresponding manuscript.</p> |
| format | Recurso digital |
| id | zenodo_https___doi_org_10_5281_zenodo_20263163 |
| institution | Zenodo |
| language | |
| publishDate | 2026 |
| publisher | Zenodo |
| record_format | zenodo |
| spellingShingle | Scientific Integrity in Artificial Intelligence: Evidence from a Horizon Europe Research Project Benítez Baleato, Jesús Manuel research evaluation AI ethics scientific integrity Horizon Europe publications corpus risk classification channel mismatch GDPR Article 9 data protection pre-registered <p>Tables from a corpus-level risk-classification analysis of the publications of an EU-funded research programme on artificial intelligence in the political-communication domain. The classification scheme — four axes (topic, venue, geography, disciplinarity), per-axis operationalisation, and risk-gradient thresholds — is documented in the companion framework documentation (see relatedIdentifiers).</p><p>In order to ensure the analysis stays at institutional level (avoiding personal attribution to researchers), the tables refer to individual publications by an opaque identifier (<code>pub_uid</code>, P001–P0NN) and contain no bibliographic information (titles, author names, venue names, DOIs). Aggregations report counts and percentages across the 75-publication corpus.</p><p>Contents (in <code>derived-tables/</code>): a per-publication enriched table (year, venue type, data source, classification levels, risk score) plus 27 aggregation tables along axes and cross-tabulations (per-axis distributions; risk by venue, by year, by discipline; channel-mismatch; ethics-authorisation and DPIA counts; year-trend; country and topic risk; LLM use; data-source and output-category breakdowns).</p><p>A subsequent version (2.0.0) will add the R analysis scripts, the rendered summary tables, the figures, and the summary statistics that consume these tables to reproduce the empirical reporting of the corresponding manuscript.</p> |
| title | Scientific Integrity in Artificial Intelligence: Evidence from a Horizon Europe Research Project |
| topic | research evaluation AI ethics scientific integrity Horizon Europe publications corpus risk classification channel mismatch GDPR Article 9 data protection pre-registered |
| url | https://doi.org/10.5281/zenodo.20263163 |