The Community Organization Refugee Sponsorship Program: Understanding Private Resettlement in New Zealand and Drawing Lessons from the Canadian System
Bibliographic record
Abstract
This thesis examines the newly introduced private resettlement program in New Zealand. The Community Organisation Refugee Sponsorship program started in 2017 and offers an additional method for refugee resettlement for quota refugees in New Zealand. The thesis uses the most similar case study comparative methodology to identify several countries with a similar private program structure as New Zealand. The cases examined were Australia, Argentina, Ireland, the United Kingdom, and Canada. The thesis conducted a lesson drawing exercise comparing New Zealand against Canada and their private resettlement programs. Canada is an exemplar case in private resettlement and offers a good comparison to glean lessons. The thesis used normative principles to examine both programs and identifies how New Zealand can develop its private program to protect the government’s international commitments from privatization. The thesis explores how the private sponsorship community, those who engage in private refugee resettlement, use these principles as standards for engagement in Canada and how this might apply to New Zealand. Using lessons in Canada, the thesis comes to several conclusions and program recommendations for the second pilot of the Community Organisation Refugee Sponsorship programme. The thesis urges the New Zealand government to fully embody the principle of Additionality into their program design and use nodal policy instruments to publicize the information. The thesis draws on lessons from the Blended Visa Office Referred program and the Private Sponsorship Refugee program as to why full incorporation of this principle is needed. The thesis urges New Zealand to expand the Naming principle and allow family reunification within the private resettlement structure. Lastly, the thesis makes recommendations for resettlement plans to increase sponsorship success.
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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 | 0.005 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.026 | 0.023 |
| Scholarly communication | 0.012 | 0.009 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.008 | 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".