I tiakina i:
| Kaituhi matua: | |
|---|---|
| Hōputu: | Recurso digital |
| Reo: | Ingarihi |
| I whakaputaina: |
Zenodo
2026
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| Ngā marau: | |
| Urunga tuihono: | https://doi.org/10.5281/zenodo.19717749 |
| Ngā Tūtohu: |
Tāpirihia he Tūtohu
Kāore He Tūtohu, Me noho koe te mea tuatahi ki te tūtohu i tēnei pūkete!
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| _version_ | 1866902170177110016 |
|---|---|
| author | Farquhar, Hayden |
| author_facet | Farquhar, Hayden |
| contents | <p>NLP pipeline for detecting fraud-associated characteristics (tortured phrases, formulaic structure, AI-generated text markers, citation anomalies, co-authorship network patterns, cross-document similarity) in the medical artificial intelligence literature. Trains a supervised classifier using Retraction Watch labels and estimates prevalence via Positive-Unlabelled learning correction. v3.0.0 adds a leave-Hindawi-out sensitivity analysis script; no changes to pre-registered classifier, weights, or feature definitions. v2.0.0 includes bug fixes identified during analysis (see CHANGELOG.md). Analysis plan pre-registered on OSF (DOI: 10.17605/OSF.IO/JB4T6). v1.0.0 (pre-registered code): DOI 10.5281/zenodo.19481250.</p> |
| format | Recurso digital |
| id | zenodo_https___doi_org_10_5281_zenodo_19717749 |
| institution | Zenodo |
| language | eng |
| publishDate | 2026 |
| publisher | Zenodo |
| record_format | zenodo |
| spellingShingle | Analysis code for: Paper Mill Subtypes in Medical AI: Multi-Signal NLP Detection Reveals Heterogeneous Fraud Fingerprints Farquhar, Hayden paper mill detection research integrity medical AI NLP bibliometrics tortured phrases positive-unlabelled learning retraction watch <p>NLP pipeline for detecting fraud-associated characteristics (tortured phrases, formulaic structure, AI-generated text markers, citation anomalies, co-authorship network patterns, cross-document similarity) in the medical artificial intelligence literature. Trains a supervised classifier using Retraction Watch labels and estimates prevalence via Positive-Unlabelled learning correction. v3.0.0 adds a leave-Hindawi-out sensitivity analysis script; no changes to pre-registered classifier, weights, or feature definitions. v2.0.0 includes bug fixes identified during analysis (see CHANGELOG.md). Analysis plan pre-registered on OSF (DOI: 10.17605/OSF.IO/JB4T6). v1.0.0 (pre-registered code): DOI 10.5281/zenodo.19481250.</p> |
| title | Analysis code for: Paper Mill Subtypes in Medical AI: Multi-Signal NLP Detection Reveals Heterogeneous Fraud Fingerprints |
| topic | paper mill detection research integrity medical AI NLP bibliometrics tortured phrases positive-unlabelled learning retraction watch |
| url | https://doi.org/10.5281/zenodo.19717749 |