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Record W4214749736 · doi:10.3138/uhr-2021-0006

Behind Strong Palings: Producing Knowledge in the Modern City at Montreal’s Emigrant Sheds, 1832–1852

2022· article· en· W4214749736 on OpenAlexaffvenueabout
Dan Horner

Bibliographic record

VenueUrban History Review · 2022
Typearticle
Languageen
FieldSocial Sciences
TopicCanadian Identity and History
Canadian institutionsToronto Metropolitan University
Fundersnot available
KeywordsColonialismSettlement (finance)Context (archaeology)PoliticsNewspaperGovernment (linguistics)Corporate governancePolitical sciencePublic administrationEconomic historyHistoryArchaeologyLawManagement

Abstract

fetched live from OpenAlex

Established during the cholera epidemic of 1832, Montreal’s emigrant sheds sat on the city’s western fringe and played a vital role in urban governance in British North America for the next twenty years. The site served as a place where migrants could be segregated from the general public until they were deemed to be sufficiently healthy to continue with the process of settlement. Public officials employed at and around the sheds and observers who visited the facility thus used the emigrant sheds as a place to consider strategies of classification and containment. The production of knowledge was central to the work that went on there. This article uses government reports, emigrant handbooks, newspapers, and private correspondence to delve into the workings of the emigrant sheds and to place them in the broader context of the politics of public health and the establishment of carceral institutions. It situates these processes at the centre of the project of settler colonialism in British North America.

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: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.070
Threshold uncertainty score0.506

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.005
Science and technology studies0.0130.018
Scholarly communication0.0060.002
Open science0.0010.003
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0070.000

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 head0.045
GPT teacher head0.258
Teacher spread0.214 · 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 designQualitative
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
Published2022
Admission routes3
Has abstractyes

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