Towards a More Effective Use of Irregular Migration Data in Policymaking
Bibliographic record
Abstract
Concerns around irregular migration have dominated media headlines across Europe, shaped recent elections, and influenced historical policy initiatives such as the new Pact on Migration and Asylum. Discussions and policymaking related to irregular migration are often heavily influenced by the latest numbers and estimates of quickly changing irregular migration trends, such as the number of border crossings or apprehensions of migrants without legal status. Such data also play an important role in advocacy, the evaluation of policies, operational planning, and efforts to foster dialogue and policy innovation. But before policymakers, practitioners, researchers, nongovernmental organisation staff, and other actors use data on irregular migration, datasets are shaped by many different stakeholders, each with their own objectives and priorities. The first step in this pathway involves defining irregular migration, after which data are collected, shared, accessed, interpreted, and disseminated. In each step—from definition to dissemination—different obstacles emerge that can hinder the effective collection and use of data to help manage migration, support communities in which irregular migrants live, and reach those migrants with essential services. Obstacles that arise earlier on in this process, for example unclear or inconsistent definitions of irregular migration or issues related to data sharing and access, can create problems down the line for data users.These obstacles’ causes and impacts are many and varied. However, EU-level and national workshops and expert interviews conducted for the MIrreM project, as well as a comprehensive literature review, point to certain common challenges: Most data collection is a byproduct of ongoing operations or reflects political priorities on issues such as border security. Available datasets therefore often do not match the data needs of policymakers and other end users, and they often have data gaps that limit policy development. Unclear and inconsistent definitions of irregular migration, meanwhile, increase the risk of data being misinterpreted and limit comparability over time and across geographies. Datasets on irregular migration also frequently do not include key information about how the data were collected and any associated data quality issues. At the same time, many actors using data on irregular migration lack the data literacy and expertise to properly assess a dataset’s quality and to interpret its contents. Many actors may also struggle to access existing data because of unclear legal regulations, technical and practical obstacles (such as a lack of interoperability between data systems), and informal data-sharing practices that heavily rely on trust and institutional relationships. Finally, even when data are available, potential data users may opt not to use them because they do not view them as suited to their needs, because they do not trust their quality and neutrality, or because they are simply not awarethe data exist.Efforts to address these challenges could begin from several starting points. These include strengthening local-level data collection, separating data collection from law enforcement functions, harmonising definitions of key concepts, and investing in users’ capacity building and data literacy. Additionally, improving the interoperability of data systems—with proper safeguards in place—and formalising data-sharing agreements could help enhance the accessibility and reliability of irregular migration data. Ultimately, while the increasing availability of data provides hope for more accurate estimates and more evidence-informed policymaking, it remains essential to approach data use with care and safeguards. Recognising the limitations of current datasets and taking steps to manage data users’ expectations will be necessary to help ensure that data serve as a tool for constructive dialogue and effective policy development, rather than a source of misinformation,fearmongering, and human rights violations.
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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 | 0.582 | 0.623 |
| Meta-epidemiology (narrow) | 0.002 | 0.005 |
| Meta-epidemiology (broad) | 0.005 | 0.004 |
| Bibliometrics | 0.025 | 0.039 |
| Science and technology studies | 0.013 | 0.038 |
| Scholarly communication | 0.079 | 0.105 |
| Open science | 0.012 | 0.047 |
| Research integrity | 0.018 | 0.027 |
| Insufficient payload (model declined to judge) | 0.014 | 0.008 |
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".