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Record W2318883844 · doi:10.32920/23739507

Paving Their Way and Earning Their Pay: Economic Survival Experiences of Immigrants in East Toronto

2023· article· en· W2318883844 on OpenAlexfundaboutno aff
Keren Gottfried, John Shields, Nasima Akter, Diane Dyson, Sevgul Topkara-Sarsu, Haweiya Egeh, Sandra Guerra

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldSocial Sciences
TopicMigration, Ethnicity, and Economy
Canadian institutionsnot available
FundersIndigenous and Northern Affairs Canada
KeywordsImmigrationEthnic groupFace (sociological concept)PopulationSociologyDemographic economicsPolitical scienceEconomics

Abstract

fetched live from OpenAlex

This paper lies at the intersection of precarious labour and immigrant employment experiences. The labour market has evolved over the past few decades such that jobs are increasingly precarious - poorly paid, insecure, and lacking in employee protections. Immigrants are overrepresented among those working precarious jobs and face compounded challenges to achieving socio-economic stability. Immigrants, especially immigrant women, experience heightened exploitation and marginalization in the process of trying to economically and socially integrate into Canadian society. The paper investigates how immigrants living in an east Toronto ethnic enclave navigates the labour market and survive precarious and informal employment realities. It makes use of a unique empirical survey of this community to help shed light on the economic lives of this population.

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)0.001
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: Empirical
Teacher disagreement score0.240
Threshold uncertainty score0.483

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0110.003
Scholarly communication0.0030.001
Open science0.0010.003
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0040.000

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 head0.032
GPT teacher head0.284
Teacher spread0.252 · 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

Citations6
Published2023
Admission routes2
Has abstractyes

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