Towards a More Effective Use of Irregular Migration Data in Policymaking
Notice bibliographique
Résumé
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.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,582 | 0,623 |
| Méta-épidémiologie (sens strict) | 0,002 | 0,005 |
| Méta-épidémiologie (sens large) | 0,005 | 0,004 |
| Bibliométrie | 0,025 | 0,039 |
| Études des sciences et des technologies | 0,013 | 0,038 |
| Communication savante | 0,079 | 0,105 |
| Science ouverte | 0,012 | 0,047 |
| Intégrité de la recherche | 0,018 | 0,027 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,014 | 0,008 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».