Bibliographic record
Abstract
Since 2014 1.8 million migrant people arrived in Europe from Italy, Spain and Greece. It represents a quarter of the London population. And other 16 thousand are the deaths in the Mediterranean Sea in four years. Yet, there are those who still talk about invasion, making it a weapon for political propaganda, and the health sector is among the most exploited. The Italian politicians themselves are at the forefront on the diffusion of fake news on the health conditions of people arriving in Europe, riding the long wave of fear of the unknown foreign people, and of the possibility of a contagion. However, data tell a completely different story: migrants who arrive in Europe does not bring vulnerability to the natives: they are vulnerable, and they need first of all our protection. There is no evidence of outbreaks of infectious diseases, and in any case border controls are carried out, and they can identify anyone who is not in good health before landing. The same can not be said of mental illness. Rape, torture, murders: this is what people arrived in Italy from Libya have lived. This should be the starting point - not the refusal to give hospitality - for aligning our communities with the UN warning from here until 2030: no one left behind.
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 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 | 0.002 | 0.012 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.004 | 0.005 |
| Scholarly communication | 0.008 | 0.005 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.009 | 0.018 |
| Insufficient payload (model declined to judge) | 0.006 | 0.003 |
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".