‘Digging Deep’ Inside Post-Migration Social Care and Caring: A Critical Realist Relational Sociological Analysis of Service Providers’ and Service Users’ Experiences in the Settlement and Integration Sector in Ontario/Canada
Bibliographic record
Abstract
This doctoral research project explores the complexities implicated in ‘modernized’ publicly-funded and regulated settlement and integration services offered at an organization located in Ontario/Canada. Specifically, it delves into the structural, cultural, agential and reflexive dynamics emerging at this particular site and manifestation of post-migration social care. It also focalizes on the nature and purpose of social relations arising from this social service architecture. It especially considers the experiences of people whose lives were contoured by refugeehood, youthhood and service userhood/clienthood. This research effort draws on participations, observations, interviews and documents from an organizational ethnography conducted in 2015, or under the Conservative Government of Stephen Harper. It uses a critical realist relational sociological framework to make sense of research data. Overall, it contributes to forced migration and post-migration social care/ work scholarship through the use of an innovative meta/theoretical lens that detains multiple and diverse intricacies that are often left out by researchers working on the newcomer-serving sector. As the title indicates, this contribution is driven by the metaphor of ‘digging deep’ that probes the ontological social realities beyond those of the directly visible/observable.
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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 | 0.007 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.040 | 0.073 |
| Scholarly communication | 0.015 | 0.007 |
| Open science | 0.003 | 0.010 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.003 | 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".