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Record W2790739046 · doi:10.5296/jsr.v9i1.12368

Beyond Good Intentions: Race Regimes, Racialisation, Immigrant Service Non-governmental Organizations (IS-NGOs) and Race-Class Reproductions in Canada

2018· article· en· W2790739046 on OpenAlexaffabout
Wanda Johnson Chell, Dip Kapoor

Bibliographic record

VenueJournal of Sociological Research · 2018
Typearticle
Languageen
FieldSocial Sciences
TopicTourism, Volunteerism, and Development
Canadian institutionsUniversity of AlbertaNorQuest College
Fundersnot available
KeywordsRace (biology)PolitySociologyImmigrationGender studiesInequalityMulticulturalismPrivilege (computing)Political sciencePoliticsLawPedagogy

Abstract

fetched live from OpenAlex

Based on research conducted in a Parenting and Literacy Program (PLP) offered by an Immigrant Service-Non Governmental Organisation (IS-NGO) located in Alberta, Canada, a racialisation and race regimes framework is deployed to advance the proposition that IS-NGOs and their approach to programs and service provision encourage race-class inequalities and augment the contemporary race regime of multiculturalism in Canada. This is in/advertently achieved by selectively racializing im/migrants and reproducing class inequities through the adherence to neoliberal prescriptions (best practices) while claiming to settle, support and work for social justice for im/migrants. We explore the structures, ideas and power relations of an IS-NGO as a race regime and its’ race-class implications for perpetuating hierarchy’s which continue to define a Canadian colonial settler society. The purpose of this research is to stimulate renewal within IS-NGOs, as an exercise in critical reflexivity and to encourage changes at the organisational and employee/practitioner level, by fostering efforts to undermine, redirect and replace race regimes and class inequality in the interests of a still emergent democratic society and polity in Canada.

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)0.005
metaresearch head score (Gemma)0.004
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.280
Threshold uncertainty score0.917

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0050.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.043
GPT teacher head0.350
Teacher spread0.307 · 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

Citations0
Published2018
Admission routes2
Has abstractyes

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