Balancing Resettlement, Protection and Rapport on the Frontline: Delivering the Resettlement Assistance Program during COVID-19
Bibliographic record
Abstract
Drawing on my experience as a general counsellor in the Resettlement Assistance Program (RAP), I explore the impact COVID-19 has had on the initial resettlement services provided for government-assisted refugees (GARs) and on frontline workers in the field. Balancing the requirement to enforce protection measures and the need to establish rapport was one of the major challenges the pandemic posed to GAR support practices. To unpack the particularities of this challenge, I give the example of two resettlement services GARs receive upon arrival: namely, resettlement orientations and children’s education. I argue that using an intersectional lens demonstrates the pandemic’s unequal effects and how they exacerbate the vulnerabilities of GARs embarking on their resettlement journey. I hold that developing COVID-19 responses informed by intersectionality opens a space for services and policies that mitigate these effects.
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 | 0.011 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.021 | 0.011 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.003 | 0.019 |
| Research integrity | 0.003 | 0.010 |
| 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".