Bibliographic record
Abstract
In Canada, the phenomenon of urban refugees is largely an expression of state-managed practices, not spontaneous migration and settlement. This study focuses on the distinctly North American, and specifically Canadian, experiences of pre-meditated, state-planned, government-managed migration and settlement for urban refugees from the Aceh region of Indonesia to Vancouver, British Columbia in 2004. It explores why and how these refugees came to Vancouver; the state policy decision that located all of them in one city; and how they have fared in acquiring official language proficiency and employment. Whereas many refugees move to urban centres to enhance educational and employment opportunities, this study illustrates the obstacles to accessing both in Vancouver. Despite full legal status and access to employment sanctioned by the host state, there is no guarantee that refugees will have an easier time creating livelihoods under dramatically new conditions. The analysis is based on research conducted between January and August 2005 during which a survey of housing, employment, and income issues was conducted with 70 of the 104 Acehnese refugees who had relocated to Vancouver since February 2004. In addition, a one-day, three-part series of focus groups was held during which 47 members of the Acehnese community took part. Discussions centred on three key moments during their migration: (1) while in Malaysian detention camps; (2) upon arrival in Vancouver, British Columbia; and (3) during the first year of settlement in the city, to ascertain common settlement experiences, policy implications, and the short-term ‘success’ of the resettlement.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.019 | 0.004 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.003 |
| 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".