Understanding Newcomer Experiences of Inclusion (UNEOI)
Bibliographic record
Abstract
Still in the pre-implementation phase, this two-year study uses a community-based, ecological approach to examine newcomers’ experiences of settlement, integration, and inclusion and how they impact well-being. To date, there has been little effort to directly measure newcomer well-being in Alberta. There are analyses of well-being-type measures contained within the national Longitudinal Survey of Immigrants to Canada (LSIC), and studies that infer well-being through the perspective of service providers, but a brief literature review does not reveal any recent well-being surveys that have directly engaged newcomers in Alberta. Another challenge is the use of one-dimensional social-and-economic indicators of well-being (e.g. GDP), that do not fully capture newcomers’ experiences of well-being. This project fills this gap in literature by using a mixed methods approach to engage newcomers and develop a measurement system that analyzes a wide range of variables that impact their subjective wellbeing (including inclusion and civic participation). Findings will contribute to the creation of new knowledge to support integration, increase inclusion, and inform the effective distribution of services across Alberta by identifying successes and gaps in service provision.
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 | 0.012 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.004 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.002 | 0.012 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 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".