Government restructuring and settlement agencies in Vancouver: bringing advocacy back in1
Bibliographic record
Abstract
Welfare state restructuring during the 1990s changed the landscape of settlement services in Vancouver, creating a more uneven geography of provision, and increasing gaps between community needs and the services available. The voluntary sector faces a potential loss of autonomy, distortion of agency mandates, dangers of increased bureaucratisation and commercialisation, greater difficulty responding to community needs, and decreasing ability to undertake advocacy, all of which potentially result in a loss of legitimacy. This chapter discusses the creation of new and diverse landscapes in the major urban centres in Canada. It presents a case study of Vancouver, illustrating that welfare state restructuring in the late 1990s fundamentally reshaped settlement services in Canada. The study examines how settlement agencies negotiated this critical period of initial restructuring, focusing on three large non-profit agencies that dominated settlement service provision in the Vancouver area: the Immigrant Services Society, the Multilingual Orientation Service Association for Immigrant Communities, and the United Chinese Community Enrichment Services Society. It is argued that restructuring changed the landscape of settlement services in several important ways. The most significant change, however, was the growing importance of what one settlement worker referred to as ‘big advocacy’.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.024 | 0.013 |
| Scholarly communication | 0.012 | 0.002 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".