Unlocking Provincial Voices: How ML/AI Enables New Narratives of Citizenship in the Early Modern Dutch Republic. The Case of Overijssel's Resolutions (1578-1795).

Early modern Dutch political historiography has long privileged Holland at the expense of the provincial periphery, leaving the political voices of petitioners and communities in regions such as Overijssel effectively silenced. This presentation introduces a ML/AI-driven pipeline — combining Handwritten Text Recognition (HTR), Named Entity Recognition (NER), and Wikidata-linked toponym resolution — developed to make the Resolutions of the States of Overijssel (1578–1795), comprising approximately 50,000 handwritten pages, accessible for historical analysis. The pipeline, realised in collaboration with Collectie Overijssel and the HAICu project, enables systematic identification of persons, dates, and places across the corpus, transforming manuscript sources into structured, spatially and temporally queryable data. Initial results — including the identification of meeting dates and linked place names — already permit preliminary analysis of the spatial and temporal patterns of provincial political participation, with fuller findings to follow as enrichment progresses. The complete dataset will be made publicly available by spring 2028, coinciding with the province's 500th anniversary, and the methodology is deliberately designed to be transferable to comparable provincial corpora across the Dutch Republic.

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Publication Details

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Zenodo (CERN European Organization for Nuclear Research)
Published
2026-04-22
DOI
https://doi.org/10.5281/zenodo.19700108
Primary Topic
Computational and Text Analysis Methods
Type
article
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article

Unlocking Provincial Voices: How ML/AI Enables New Narratives of Citizenship in the Early Modern Dutch Republic. The Case of Overijssel's Resolutions (1578-1795).

Andreas Weber, C. A. (Annemieke) Romein, Jos Mooijweer
Zenodo (CERN European Organization for Nuclear Research)
Computational and Text Analysis Methods
article

Unlocking Provincial Voices: How ML/AI Enables New Narratives of Citizenship in the Early Modern Dutch Republic. The Case of Overijssel's Resolutions (1578-1795).

Andreas Weber, C. A. (Annemieke) Romein, Jos Mooijweer
article en

Abstract

Early modern Dutch political historiography has long privileged Holland at the expense of the provincial periphery, leaving the political voices of petitioners and communities in regions such as Overijssel effectively silenced. This presentation introduces a ML/AI-driven pipeline — combining Handwritten Text Recognition (HTR), Named Entity Recognition (NER), and Wikidata-linked toponym resolution — developed to make the Resolutions of the States of Overijssel (1578–1795), comprising approximately 50,000 handwritten pages, accessible for historical analysis. The pipeline, realised in collaboration with Collectie Overijssel and the HAICu project, enables systematic identification of persons, dates, and places across the corpus, transforming manuscript sources into structured, spatially and temporally queryable data. Initial results — including the identification of meeting dates and linked place names — already permit preliminary analysis of the spatial and temporal patterns of provincial political participation, with fuller findings to follow as enrichment progresses. The complete dataset will be made publicly available by spring 2028, coinciding with the province's 500th anniversary, and the methodology is deliberately designed to be transferable to comparable provincial corpora across the Dutch Republic.

Zenodo (CERN European Organization for Nuclear Research)
University of Bern (CH), Viaa Christian University of Applied Sciences (NL), University of Twente (NL)
Reduced inequalities
Openalex Percentile: Top 3%
Computational and Text Analysis Methods
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Unlocking Provincial Voices: How ML/AI Enables New Narratives of Citizenship in the Early Modern Dutch Republic. The Case of Overijssel's Resolutions (1578-1795). — Andreas Weber, C. A. (Annemieke) Romein, et al. · Zenodo (CERN European Organization for Nuclear Research) (2026) | TGRS Research Map | TGRS