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Record W4244823498 · doi:10.32920/ryerson.14664639

Thinking third sector in Canadian and German settlement and social inclusion

2021· preprint· en· W4244823498 on OpenAlexafffundabout
Riley Rose Bushell

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldSocial Sciences
TopicLabor Movements and Unions
Canadian institutionsToronto Metropolitan University
FundersMitacs
KeywordsGermanSettlement (finance)ImmigrationRestructuringInclusion (mineral)Political scienceWelfare stateContext (archaeology)PoliticsImmigration policySocial policyEconomic growthEconomyPublic administrationPolitical economySociologyGeographyEconomicsSocial scienceLaw

Abstract

fetched live from OpenAlex

Canada and Germany have become major immigrant-receiving countries of the Global North, sharing settlement structures shaped by federal, regional and municipal governments and operated by large and diverse third sectors. Through an integrative literature review, this study critically examines the Canadian and German third sectors involved in settlement and social inclusion initiatives, particularly in the context of neoliberal policymaking prevalent in both countries since the 1980s. First outlining the structure and landscape of settlement in each country, it identifies several shared challenges stemming from neoliberal federal policy and the retreat of the national welfare state. Filling literature gaps in this field is particularly important given recent increases in asylum-seeking in Germany, and the global emergence of right-wing, anti-immigration political movements. The purpose of this study is to serve as the basis for further cross-national consideration, discussion and mutual learning between Canada and Germany. Key words: Canada, Germany, third sector, settlement sector, social inclusion, asylum, immigration, neoliberal policy and restructuring

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)0.003
metaresearch head score (Gemma)0.004
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.148
Threshold uncertainty score0.988

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0040.006
Science and technology studies0.0180.017
Scholarly communication0.0110.003
Open science0.0010.006
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0050.000

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 head0.018
GPT teacher head0.306
Teacher spread0.288 · 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

Citations0
Published2021
Admission routes3
Has abstractyes

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