Current methodological approaches in studying the use of advanced digital technologies in migration management
Bibliographic record
Abstract
The use of technology in migration management has increased in recent years. As such, practitioners and scholars are increasingly interested in the real and potential use of advanced technologies in migration management. This paper offers an early review of academic and gray literature on the use of advanced digital technologies (ADTs) in migration management processes. The primary focus of this review is literature that discusses migration management technologies—ADTs used by institutional actors (governments, NGOs, transnational institutions). This paper is divided into four thematic areas, aimed at providing a summary of major trends in the literature, including research methodologies, types of technologies, purpose of technologies and the migrants impacted by the technologies explored. Based on the literature reviewed, we identify common themes and areas that merit further exploration and research. To close, we offer an early view into the current uses of advanced digital technologies that we have identified in our Migration Tech Tracker, an interactive tool that consolidates the information found in the literature review of the paper including the various uses of technology by the diverse range of actors in the migration sector. The paper leverages the information from the Tracker to both indicate where and how emerging technologies are being used to govern migrants and simultaneously to identify ADTs that are being analyzed, reported on and researched and those that remain underexplored.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".