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Record W7094266565

Perspectives on youth - healthy Europe : confidence and uncertainty for young people in contemporary Europe

2016· book-chapter· en· W7094266565 on OpenAlexaboutno aff

Bibliographic record

VenueOAR@UM (University of Malta) · 2016
Typebook-chapter
Languageen
FieldSocial Sciences
TopicYouth Education and Societal Dynamics
Canadian institutionsnot available
Fundersnot available
KeywordsNothingQuarter (Canadian coin)PopulationMillerExistentialismImmigrationVariety (cybernetics)
DOInot available

Abstract

fetched live from OpenAlex

Homo Migratus. A term I coined to make a point – an important point: human beings move. It is what we have always done; it is nothing new. Indeed, contemporary trends indicate that international migration is now an integral part of globalisation. This, according to Castles and Miller (2009) is the “Age of Migration”. But what is the “Age” of migration? The UN Youth Report of 2013 suggested that by mid-2010, the global number of international migrants aged 15-24 was estimated to be around 27 million, making up around one eighth of the global migrant population (estimated at that time to be around 214 million). According to another UN report, young people aged 19-29 constitute somewhere between 36% and 57% of international migrants (United Nations 2013). Young people move for a variety of reasons, be it for education, employment opportunities, voluntary work abroad, for love even. There are also those who are forced to flee their home as a result of an existential threat. Statistics on asylum claims throughout the EU are significant. In 2014, almost four in every five asylum-seekers in the EU-28 were under 35 years of age (79%). Those aged 18-34 made up just over half of the total number of applicants (54%), while minors under the age of 18 accounted for just over one quarter (or 26%). In 2014, more than 23 000 unaccompanied minors (UaMs) requested asylum in one of the EU-28 countries (Eurostat 2015).

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: none
Teacher disagreement score0.799
Threshold uncertainty score0.858

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
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 head0.053
GPT teacher head0.273
Teacher spread0.221 · 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
GenreOther

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
Published2016
Admission routes1
Has abstractyes

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