MétaCan
Menu
Back to cohort
Record W2061924720 · doi:10.7202/030953ar

Urban Migration in Imperial Germany: Towards a Quantitative Model

2006· article· en· W2061924720 on OpenAlexvenueno aff
Steve Hochstadt

Bibliographic record

VenueHistorical Papers · 2006
Typearticle
Languageen
FieldSocial Sciences
TopicUrbanization and City Planning
Canadian institutionsnot available
Fundersnot available
KeywordsModernization theoryIndustrialisationUrbanizationConstruct (python library)Internal migrationGeographyEconomic geographyDemographic economicsDevelopment economicsSociologyEconomic growthPopulationPolitical scienceDemographyEconomicsComputer science

Abstract

fetched live from OpenAlex

Migration research has been dominated by broad assumptions which this paper brings into question. Modernization theory holds that rural-urban migration was one-way, permanent, and continually increasing during industrialization. Nominal-level research on urban populations tends to accept the idea that nonnatives are becoming permanent residents. Data from Germany show reality to have been quite different. Urban mobility peaked around 1900, and has fallen steadily since then. The great majority of urban inmigrants soon left the city, mainly returning to their rural origins. Thus a new model of urban migration is needed. This model must lake into account certain structural characteristics of urban migrants in Germany. Males were more mobile than females, but the differences lay primarily among unmarried adults, whose mobility rates were at least five times those of families. Mobility was inversely proportional to income: workers and domestics were several times as mobile as professionals and the self-employed. The paper does not construct a new model of migration but uses these data to raise questions which might lead to such a model.

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)0.003
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.022
Threshold uncertainty score0.043

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0010.002
Scholarly communication0.0030.002
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0050.000

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 head0.027
GPT teacher head0.278
Teacher spread0.251 · 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 designSimulation or modeling
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

Citations1
Published2006
Admission routes1
Has abstractyes

Explore more

Same venueHistorical PapersSame topicUrbanization and City PlanningFrench-language works237,207