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Record W4235393652 · doi:10.3138/chr.92.2.231

Managing Migrants: Toronto, 1820–1880

2011· article· en· W4235393652 on OpenAlexvenueaboutno aff
Lisa Chilton

Bibliographic record

VenueCanadian Historical Review · 2011
Typearticle
Languageen
FieldSocial Sciences
TopicCanadian Identity and History
Canadian institutionsnot available
Fundersnot available
KeywordsImmigrationState (computer science)Period (music)Settlement (finance)Political scienceGovernment (linguistics)Power (physics)GeographyEconomic historyHistoryLawBusiness

Abstract

fetched live from OpenAlex

Abstract: Immigration to and through central Canada increased substantially in the middle decades of the nineteenth century. In response to some of the problems associated with this mass migration, and in an effort to stimulate more of the ‘right’ kind of settlement, state-funded immigration agencies were established at all major ports and urban reception centres across the region during this period. To date, most of the literature on this subject has focused upon the state's management of migrants in Lower Canada (at Montreal, Quebec, and Grosse-Île) and upon the response of government officials to the period's major epidemics (cholera and typhus). This article uses Toronto as a case study to trace the evolution of the state's interaction with migrants from a different starting point. It emphasizes the importance of the 1820–80 period – a period in which major state initiatives were put in place to regulate the flow of immigration more effectively. It underlines the fact that the state consisted of multiple, frequently competing layers of authority and power during the period of transition from colonies to nation. Finally, the study of Toronto highlights that the intersections of different state levels (municipal, provincial, imperial, federal) did not constitute an especially monolithic state regulatory response during this period, but rather more of a labyrinth whose changing features could radically affect the individual experiences of migrants during these years.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Review · Consensus signal: none
Teacher disagreement score0.315
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0160.001

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 head0.041
GPT teacher head0.250
Teacher spread0.208 · 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; both teacher heads agree on what is shown here.

Study designNot applicable
Domainnot available
GenreReview

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

Citations3
Published2011
Admission routes2
Has abstractyes

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