Bibliographic record
Abstract
Migration, Migrants and Cultural Development "An immigrant is someone who lost his mother," (5) wrote Julia Kristeva, a famous contemporary French cultural and literary critic and psychoanalyst, herself originally an immigrant from Bulgaria.Her definition of an immigrant captures the universal trauma of migration.With her peculiar psychoanalytic negativity, Kristeva, having witnessed tragic consequences of the 20th-century migrations, rejected the positive meanings of migration and its historical benefit, for migration has traditionally manifested itself as a great impetus for cultural, economic, social and political development.Migrations have given rise to ancient Egypt, Assyria, Babylonia, Persia, the Tyrean Empire, Carthage, Greece and Rome, the New Carthage.The migrating Phoenicians founded the colony of Cadiz in Spain some 80 years after the Trojan War; on the way to Spain, the migrants from Tyre developed Motya, Salunto and Palermo.According to Maria Aubert and her archeological studies, whose results were published in 1986, large remnants of Phoenician settlements had been found in Italy at Nora, Sulcis, Bithia and Calarsis (now Cagliari).Virgil's story of the rejected love of Dido and the Aenean voyage to the ancient Latium poetically recreates the migration of the Phoenicians from their originally tiny twelve-mile long outpost to the later sixteen thousand-mile long Empire, which used to tie Spain, Portugal and Italy to Asia and the Caucasus region.Allegedly, the name of the island of Sardinia derives from the legendary Tyrean hero Sardus.In 878 BCE, about one hundred twenty five years before the foundation of Rome, the monumental Carthage had been established.As early as the 12t* century BCE, the Phoenician migrants had transformed the graphic signs of Ugarit into the twenty two letters of the conventional widely spread alphabet, later used by the Etruscans, Greeks, Romans and deployed in all Romance and Germanic, and most European languages afterwards.
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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.016 | 0.005 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 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".