Welcoming Infrastructures: Designing for Accountability in the Settlement Service Work in Canada
Bibliographic record
Abstract
Immigration and settlement policies, much like organizational policies or digital technology policies, are built from the coming together of discourses, tools, and people—people standing behind service desks, using databases and making decisions. These interdependent systems, beliefs, and tools effect how decisions are shaped as well as the information and data cultures of an organization (or a whole sector). Digital technologies and commitments to evidence-based, accountable service delivery are critical to the capacity of the settlement organizations to manage data flow. What does accountability mean in settlement service work? What practices and systems underpin the settlement workers’ approach to accountability efforts? What implications do these practices and systems have for the settlement service work? The dissertation addresses these questions through a multi-sited study with the settlement organizations that serve refugees and immigrants in Canada, a country often recognized as a global leader in immigration and settlement policies. This dissertation investigates the underlying data cultures while also nuancing the role of temporality in maintaining accountability practices within settlement service work. I show how data cultures and practices in the settlement organizations are entangled with political domains, administrative techniques, and material artifacts. Applying the lenses of temporality and practice theory, I demonstrate how temporality (inherent in contemporary policy-making and organizational structures) shapes the trajectories and limits of data culture and related practices, and how workers collectively define and find alternative ways to count service data and to account for immigrant settlement support. By teasing out the tensions and complexities inherent in accountability relationships, this dissertation demonstrates the intertwined nature of the digital transformation of settlement organizations—particularly the dual processes of datafication and digitalization—and sustainable immigrant integration. I find that designing equitable and sustainable settlement services will eventually necessitate more comprehensive accountability measures that consider the impact of socio-digital inequities on people’s personal, social, and professional lives.
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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 | 0.013 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.082 | 0.026 |
| Scholarly communication | 0.019 | 0.006 |
| Open science | 0.004 | 0.013 |
| Research integrity | 0.003 | 0.005 |
| 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".