Women and migrations in Croatia: from marginal subjects (“White Widows”) to contemporary migrants in the EU
Bibliographic record
Abstract
The presentation is based on the results of the ethnographic research conducted by the author in the period between 2005 and 2013. The multi-sited ethnography research started in the mountainous region (Lika) with a tradition of emigration. In the first half of the 20th century, young men migrated as part of temporary economic migrations (to the USA and Canada). Women would remain at home and live in extended familiesruga. Some of the men would not return for decades. The local community called their wives “white widows”. In mid-20th century people migrated from rural areas into cities. The author focuses her research on several families who moved to one city and its area. In the socialist period, migrant workers or “gastarbeiter”, mostly men, from nearly every family moved to Germany. Since the women were living their lives in nuclear families, they took over the paternal role, as well. After the 1990s, in post-war Croatia, many companies failed and unemployment was on the rise due to social and economic changes. Due to the feminization of labour and the feminization of migration into the EU, as well as due to the current economic crisis, unemployed women from these families, leave their families for several months to work in EU. Narrations on their individual experiences and daily lives through all types of migrations, uncover, among other things, the creation and maintenance of transnational social networks and (temporary?) changes in established family life patterns. The paper also uses demographic statistical data and media discourse analysis.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.010 | 0.006 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".