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Record W2892008170 · doi:10.3138/uhr.45.02.01

Mapping Work in Early Twentieth-Century Montreal: A Rabbi, a Neighbourhood, and a Community

2017· article· en· W2892008170 on OpenAlexvenueaboutno aff
Mary Anne Poutanen, Jason Gilliland

Bibliographic record

VenueUrban History Review · 2017
Typearticle
Languageen
FieldSocial Sciences
TopicMigration, Ethnicity, and Economy
Canadian institutionsnot available
Fundersnot available
KeywordsJudaismImmigrationEthnic groupSociologyNeighbourhood (mathematics)Settlement (finance)Gender studiesGeographyAnthropologyArchaeology

Abstract

fetched live from OpenAlex

Rabbi Simon Glazer’s 1909 daily journal provides a window onto his role as an orthodox rabbi of a largely Yiddish-speaking immigrant community, his interactions with Jewish newcomers, the range of tasks he performed to augment the inadequate stipends he received from a consortium of five city synagogues where he was chief rabbi, and the ways in which Jewish newcomers sought to become economically independent. Using a multidisciplinary methodology, including Historical Geographic Information Systems (HGIS), Glazer’s journal offers a new lens through which to view and map the social geography of this community. Our study contributes to a growing body of literature on immigrant settlement, which has shown that such clustering encouraged economic independence and social mobility. Characterized by a high degree of diversity in ethnicity and commerce, the St. Lawrence Boulevard corridor was an ideal location for Jewish newcomers to set down roots. We argue that the community served as a springboard for social mobility and that Simon Glazer played an important role at a critical moment in its early development. It was on its way to becoming one of Canada’s most significant Jewish communities. Over the eleven years that he worked in Montreal (1907–18), Glazer carved out a vital place for himself in the city’s Jewish immigrant community and honed skills that would serve him well when he returned to the United States.

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)0.002
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation 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: none
Teacher disagreement score0.597
Threshold uncertainty score0.965

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
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)0.0000.000

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 head0.061
GPT teacher head0.284
Teacher spread0.222 · 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 teacher head, 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

Citations3
Published2017
Admission routes2
Has abstractyes

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