Perspectives on youth - healthy Europe : confidence and uncertainty for young people in contemporary Europe
Bibliographic record
Abstract
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).
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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 | 0.016 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.018 | 0.035 |
| Scholarly communication | 0.024 | 0.018 |
| Open science | 0.002 | 0.020 |
| Research integrity | 0.008 | 0.017 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".