Bibliographic record
Abstract
In the years after the Second World War, the city of Rijeka found itself caught in the middle of various migratory trajectories. The departure of locals who self-identified as Italians and opted for Italian citizenship occurred simultaneously with other population movements that drained the city of inhabitants and brought in newcomers. Many locals defected and traveled to Italy, which was either their final destination or a country they transited through before being resettled elsewhere. Furthermore, after the war ended, workers from other Yugoslav areas started arriving in the city. A flourishing economy proved capable of attracting migrants with promises of good living standards; however, political reasons also motivated many to move to this Adriatic city. The latter was the case for former economic emigrants who decided to return to join the new socialist homeland and for Italian workers who symbolically sided with the socialist Yugoslavia. Rijeka was not simply a destination for many migrants—it was also a springboard for individuals from all over the Yugoslav Federation to reach the Western Bloc. This article argues that examining these intertwining patterns together rather than separately offers new insight into the challenges the city experienced during its postwar transition.
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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 | 0.001 |
| Science and technology studies | 0.011 | 0.006 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.011 | 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".