Equity and Consistency: Resettlement Needs Assessment and Referral Service Standards for Government-Assisted Refugees
Bibliographic record
Abstract
The dissertation-in-practice (DiP) addresses the equity and consistency of the resettlement needs assessment and referral service standards for government-assisted refugees (GARs) within the confines of defined autonomy and equity, diversity, and inclusion at Safe Haven Refugee Resettlement Sector, a midsize national resettlement sector in Canada. The current service standards are inequitable and inconsistent, and their application marginalizes GARs. In the DiP, I adopt and adapt an integrated Euro-Afro-Indigenous Ubuntu transformative leadership approach rooted in a decolonizing lens and embedded in equity by addressing inequitable social conditions of a community-in-practice service that causes injustices. The change implementation plan, enacted on the premise of collaborative governance and collectivism, is articulated to invite Safe Haven Refugee Resettlement Sector employees to share their voices equitably to address the product, process, and human-centric change from a regional-specific perspective in the multicultural diaspora of global GARs. As the daughter of South Africa and adopted daughter of Canada, my experience as a marginalized woman of colour has taught me that there is no path to social justice in addressing this problem of practice: Social justice is the path in this DiP to achieve equity because without embracing the social justice path, the dream and hope of achieving service equity for all GARs becomes unclear. The equitable change journey ahead is filled with humility, compassion, and empathy and will be beneficial for the resettlement service agencies administering the service standards and global GARs receiving service standards equity.
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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 | 0.083 | 0.124 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.010 | 0.010 |
| Scholarly communication | 0.016 | 0.008 |
| Open science | 0.004 | 0.021 |
| Research integrity | 0.003 | 0.007 |
| Insufficient payload (model declined to judge) | 0.003 | 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".