MétaCan
Menu
Back to cohort
Record W4410779698 · doi:10.1080/02684527.2025.2506279

Women, war and intelligence in Ypres and the Flemish West Quarter (1488–1489)

2025· article· en· W4410779698 on OpenAlexaboutno aff
Lisa Demets

Bibliographic record

VenueIntelligence & National Security · 2025
Typearticle
Languageen
FieldArts and Humanities
TopicReformation and Early Modern Christianity
Canadian institutionsnot available
Fundersnot available
KeywordsFlemishQuarter (Canadian coin)Political scienceAncient historyHistoryArchaeology

Abstract

fetched live from OpenAlex

This article sheds light on the often-overlooked roles women played in the intelligence networks of Flanders during the war against Maximilian of Austria in 1488–1489, by focussing on the intelligence activities of women in the Flemish West Quarter compensated by the city of Ypres. It also explores the growing professionalisation of these women, noting how the same women were employed repeatedly, reflecting the establishment of more structured intelligence operations by 1489. However, this did not necessarily result in a clear differentiation between the roles of intelligence work and other tasks, such as delivering letters. The article also contrasts the roles of rural and urban women employed by the city government. The critical yet under-recognised contributions of women, particularly those in rural areas, to this period of warfare show how the involvement of women in intelligence work was not solely an urban or noble phenomenon in the Middle Ages.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.062
Threshold uncertainty score0.124

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0060.005
Scholarly communication0.0030.002
Open science0.0000.003
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0060.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.019
GPT teacher head0.258
Teacher spread0.239 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations0
Published2025
Admission routes1
Has abstractyes

Explore more

Same venueIntelligence & National SecuritySame topicReformation and Early Modern ChristianityFrench-language works237,207