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Record W4392644635 · doi:10.1111/imig.13245

Transforming settlement and integration services during a pandemic

2024· article· en· W4392644635 on OpenAlexafffundabout
Valerie Preston, John Shields, Jayesh D’Souza

Bibliographic record

VenueInternational Migration · 2024
Typearticle
Languageen
FieldHealth Professions
TopicEmployment and Welfare Studies
Canadian institutionsToronto Metropolitan UniversityYork University
FundersSocial Sciences and Humanities Research Council of Canada
KeywordsWorkforceGovernment (linguistics)Public relationsPandemicImmigrationSettlement (finance)Resilience (materials science)BusinessPsychological resilienceCoronavirus disease 2019 (COVID-19)Public administrationPolitical scienceEconomic growthMedicineFinancePsychology

Abstract

fetched live from OpenAlex

Abstract Settlement services are key to Canada's success in welcoming and integrating immigrants. Offered mainly in person prior to COVID‐19 by non‐governmental agencies reliant on and regulated by government funders, services were forced online and delivered by staff working remotely. We document this transition between September 2020 and September 2021 in Ontario, Canada and the conditions that influenced it. Surveys completed by workers and managers at member agencies of the Ontario Council of Agencies Serving Immigrants reveal how agencies provided services and stabilized organizational resources and capacities. Their success is evident in staff satisfaction with management's responses to the pandemic. While our findings underscore the resilience of the agencies and their workforce, they also challenge many tenets of New Public Management. The survey and discussions with managers suggest that sustained and flexible funding, rapid and respectful communication between agencies and funders and collaborations with other agencies were key to overcoming pandemic challenges.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.252
Threshold uncertainty score0.274

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 studies0.0000.000
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 head0.034
GPT teacher head0.394
Teacher spread0.360 · 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 designObservational
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

Citations4
Published2024
Admission routes3
Has abstractyes

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