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Record W2922355162 · doi:10.2478/auseur-2018-0011

Migration in Sub-State Territories with Historical-Linguistic Minorities: Main Challenges and New Perspectives

2018· article· en· W2922355162 on OpenAlexaboutno aff
Roberta Medda-Windischer

Bibliographic record

VenueActa Universitatis Sapientiae European and Regional Studies · 2018
Typearticle
Languageen
FieldSocial Sciences
TopicPolitical Systems and Governance
Canadian institutionsnot available
Fundersnot available
KeywordsState (computer science)Diversity (politics)PopulationMinority groupPolitical scienceGeographyEthnic groupDevelopment economicsSociologyGender studiesDemographyLaw

Abstract

fetched live from OpenAlex

Abstract Migration is an important reality for many sub-national autonomous territories where traditional-historical groups (so-called ‘old minorities’) live such as Flanders, Catalonia, South Tyrol, Scotland, Basque Country, and Quebec. Some of these territories have attracted migrants for decades, while others have only recently experienced significant migration inflow. The presence of old minorities brings complexities to the management of migration issues. Indeed, it is acknowledged that the relationship between ‘old’ communities and the ‘new’ minority groups originating from migration (so-called ‘new minorities’) can be rather complicated. On the one hand, interests and needs of historical groups can be in contrast with those of the migrant population. On the other hand, the presence of new minorities can interfere with the relationship between the old minorities and the majority groups at the state level and also with the relationship between old minorities and the central state as well as with the policies enacted to protect the diversity of traditional groups and the way old minorities understand and define themselves. The present lecture analyses whether it is possible to reconcile the claims of historical minorities and of new groups originating from migration and whether policies that accommodate traditional minorities and migrants are allies in the pursuit of a pluralist and tolerant society.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.704
Threshold uncertainty score0.996

Codex and Gemma teacher scores by category

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

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.

Opus teacher head0.043
GPT teacher head0.260
Teacher spread0.217 · 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; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designQualitative
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

Citations1
Published2018
Admission routes1
Has abstractyes

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