Beyond Good Intentions: Race Regimes, Racialisation, Immigrant Service Non-governmental Organizations (IS-NGOs) and Race-Class Reproductions in Canada
Bibliographic record
Abstract
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 machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.020 | 0.012 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".