The Invisibilised Labour of Diasporas as Co-sponsors in Refugee Sponsorship: Lessons <i>From</i> Canada
Bibliographic record
Abstract
Abstract For almost 45 years, civil society groups have volunteered their time, energy, and finances to resettle more than 327,000 refugees through Canada’s Private Sponsorship of Refugees programme. Sponsorships are commonly arranged by local communities, faith-based organisations, or private citizens who have entered into agreements with the federal government. Much of this effort is supported by former refugees who were themselves resettled to Canada. Yet, the existing literature underrepresents the crucial role of sponsors with refugee histories. This research examines the previously invisibilised labour of diasporic sponsors, highlighting the unique and vital role stemming from their dual social locations as former refugees and private sponsors. Through participant testimony from in-depth, semi-structured interviews and triangulated document analysis of policy and programmatic data, this research finds that invisibilisation lies at the administrative level of sponsorship processes. This includes the interactions between Sponsorship Agreement Holders and co-sponsor mechanisms, and how formalised and less formalised processes play out. The co-sponsorship model illuminates the nuances and possibilities for sponsorship sustainability beyond the courte durée, emphasising the vital labour of diasporic sponsors in this dynamic.
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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 | 0.010 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.037 | 0.029 |
| Scholarly communication | 0.016 | 0.006 |
| Open science | 0.002 | 0.013 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.005 | 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".