CENTRALITY OF ITALY IN THE MEDITERRANEAN MIGRATION SYSTEM: ASPECTS OF FOREIGNERS’ PRESENCE
Bibliographic record
Abstract
Nowadays the Mediterranean confirms itself, much more significantly than in the past, as a place where considerable migration flows take place as well as a geographical area that holds together emigration countries but also immigration and transit countries. There is a more widespread perception of being part of a World in constant motion whose actors are being driven by factors of different nature, all of which linked by a common cause: the desire – and in some cases the need – to find elsewhere better life conditions 2 . In this sense, the area of the Mediterranean Basin represents a case of high interest since it constitutes, together with North America (USA and Canada), one the main areas of attraction for long-range as well as short and medium range flows that originate, mostly, from the areas commonly defined as developing countries. The end of the Seventies, following the oil crisis (1973), triggered a period of recession which induced all developed countries to reassess their economic strategies, generating a complete transformation of the global economy, including the Euro-Mediterranean one. In this context, a new phase of the Mediterranean migration process has unfolded bringing, among others, changes regarding the direction of the flows, set off by the adoption of “stop” policies by the European Governments (such as France and UK) as a restrictive measure to the foreign labour force demand. This type of policy, set up in order to stop migration flows
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.000 |
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.
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".