Bibliographic record
Abstract
This study inquires into the perspectives of the many groups who collect, manage, and use data generated as people migrate and settle in Canada. While their notions of care for communities and data may sometimes conflict, a range of stakeholders collaborate in their activities with data on immigration, including settlement service providers, migrant justice activists, immigration researchers, government staff and policymakers, and designers of digital systems that gather newcomers’ data. As a connected yet distanced collective of stakeholders whose practices with data influence one another and the newcomers they study or serve, these same stakeholders also enact changes in their ways of using data and digital technologies in the context of experimentation with big data analytics, automation, and greater demands for data-based reporting and sharing. To this end, this research joins practical and theoretical discussions by working to strengthen webs of relations with greater capacity for care, informed reflection, and responsibility in the use of communities’ data. Based on the lens of care, this project advances a critical approach to drastic shifts in information practices across areas of contemporary life, of which migration is a particularly pressing issue.
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 | 0.040 | 0.091 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.038 | 0.023 |
| Scholarly communication | 0.021 | 0.021 |
| Open science | 0.003 | 0.032 |
| Research integrity | 0.005 | 0.007 |
| Insufficient payload (model declined to judge) | 0.007 | 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".