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Record W2163848505 · doi:10.3138/cjh.48.2.223

The Emergence of Concentrated Settlements in Medieval Western Europe: Explanatory Frameworks in the Historiography

2013· article· en· W2163848505 on OpenAlexvenueno aff
Daniel R. Curtis

Bibliographic record

VenueJournal of History · 2013
Typearticle
Languageen
FieldArts and Humanities
TopicMedieval Literature and History
Canadian institutionsnot available
Fundersnot available
KeywordsHuman settlementHistoriographySettlement (finance)Coercion (linguistics)UrbanizationResource (disambiguation)Explanatory powerEconomic geographyEconomyHistorySociologyPolitical scienceGeographyArchaeologyEconomicsEconomic growthEpistemology

Abstract

fetched live from OpenAlex

There is now a general scholarly consensus that the concentration of rural people into settlements in Western Europe (as opposed to dispersed or scattered habitations across the countryside) occurred in various stages between the eighth and twelfth centuries, though with regional divergences in precise timing, speed, formation, and intensity. What is clear from the literature is that a “one-size fits all” model for settlement development across Western Europe is not possible. Concentrated settlements appeared in certain parts of Europe for different reasons. This article discusses the strengths and limitations of four of the most influential frameworks for explaining patterns of medieval settlement concentration and their relation to social and economic change. The frameworks under analysis emphasize, respectively, power, coercion and lordship; communalism and territorial formalization; field systems and resource management; and urbanization and market-integration.

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)0.002
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.014
Threshold uncertainty score0.028

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0040.005
Science and technology studies0.0030.022
Scholarly communication0.0040.003
Open science0.0010.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.021
GPT teacher head0.221
Teacher spread0.200 · 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 designTheoretical or conceptual
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

Citations10
Published2013
Admission routes1
Has abstractyes

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