The Digital Im/migrant: IS in Migration Governance, Work, and Life
Bibliographic record
Abstract
This panel explores how digital technologies shape the migration experience across governance, labor, and decision-making. While digital systems increasingly organize citizens’ lives, their role in shaping migrants’ experiences is less visible, especially for those navigating unfamiliar institutions and limited legal status. Drawing on empirical research, this panel examines tensions in how digital infrastructures affect migrant-state relations and platform-mediated labor markets. Panelists address topics such as the modernization of immigration institutions, social media use in migration planning, digital labor recruitment across the Americas, and algorithmic control in gig work. In dialogue with core IS concerns—sociotechnical systems, platform governance, and information-seeking behavior—the panel situates digital migration systems within broader political, structural, and ethical contexts. This interdisciplinary session fosters debate on tensions between technological innovation and equity. By tackling these issues, the panel invites IS scholars to see migration as a vital context for rethinking inclusion, infrastructure, and institutional transformation.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.004 | 0.005 |
| Scholarly communication | 0.008 | 0.006 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.031 | 0.003 |
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".