Interdependence in information practices: differences matter when caring for immigration data in Canada
Bibliographic record
Abstract
Purpose Service providers, government agencies and other entities gather data on immigration and settlement for myriad reasons. In Canada, newcomers to the country are required to provide personal information to access essential services from community-based organizations and government agencies. Individuals who handle immigration data hold valuable yet under-examined perspectives on these data collection and sharing activities. This study therefore seeks to answer the overarching question: What information practices are prominent in the work of different groups who collect, analyze and steward newcomers' data? Design/methodology/approach Our interview-based study reports on the practices of individuals supporting immigration and settlement (i.e. settlement service providers, migrant justice activists, immigration researchers, government staff and designers of digital systems and services oriented toward newcomers) through their use of newcomers' data. Findings A dual narrative and thematic analysis interprets participants' reflections on their information practices and responsibilities, showcasing variation despite their interdependence and shared priorities for newcomers' well-being. We propose the concept of “data care” to draw attention to experiences and tensions inherent in stewarding newcomers' data. This inquiry reveals conflicts over responsibilities, differences in ethical reasoning and the need for multi-stakeholder negotiation. Originality/value Findings bring greater clarity to the intricacies of respecting migrants and their privacy. The study contributes to a theoretical lens on information practices in care work by drawing from feminist care ethics and sociotechnical scholarship.
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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 | 0.014 | 0.042 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.036 | 0.019 |
| Scholarly communication | 0.016 | 0.005 |
| Open science | 0.003 | 0.011 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 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".