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Record W6930373642 · doi:10.5281/zenodo.12565528

Compilation of 20 Country Briefs on Irregular Migration Policy Context

2024· article· en· W6930373642 on OpenAlexaffabout

Bibliographic record

VenueZenodo (CERN European Organization for Nuclear Research) · 2024
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicInnovative Approaches in Technology and Social Development
Canadian institutionsToronto Metropolitan University
FundersUK Research and Innovation
KeywordsDeliverableContext (archaeology)Relevance (law)Representation (politics)Policy analysisAcknowledgementNational Policy

Abstract

fetched live from OpenAlex

This deliverable provides an overview of the national policy landscape related to migrant irregularity for 15 countries in the form of Country Briefs. Briefs are provided for the following countries: Austria, Belgium, Canada, Finland, France, Greece, Ireland, Italy, the Netherlands, Poland, Portugal, Spain, Türkiye, the United Kingdom and the United States. The Briefs are drawn from more extensive analysis conducted within MIrreM Country Profiles, which themselves will be analysed comparatively in a subsequent Deliverable (D3.3). The purpose of the Briefs is to provide a concise synopsis of the main policy issues of relevance for the irregular migration situation in each respective country, with a focus on developments since 2010. They are not foreseen to be an exhaustive representation of the entire policy and irregular migration situation in each country, but rather provide a snapshot of the most relevant issues to date, by each national rapporteur's assessment. Each Brief provides a short overview of the recent (since 2010) policies of relevance for irregular migration, in particular the respective country's irregular migration-related policy priorities, stakeholders, recent policy measures, main turning points in policy development, policy impacts, and challenges in implementation. Moreover, each Brief outlines the main types of migrant irregularity that emerge and the pathways into and out of irregularity in each country, including regularisations as relevant. Annexed to this Deliverable is also an overview of the legal and policy frameworks highlighted as relevant for migrant irregularity in each respective country.

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 imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.885
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.002

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.

Opus teacher head0.032
GPT teacher head0.242
Teacher spread0.210 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; both teacher heads agree on what is shown here.

Study designNot applicable
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2024
Admission routes2
Has abstractyes

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