Argentine Migrants to Spain and Returnees: A Case for Accumulation of Civic Assets
Bibliographic record
Abstract
Abstract Conceptually, this paper relies on the asset accumulation framework and identifies its relevance to work on Argentine migrants to Spain and returnees. The asset accumulation framework represents an innovative approach to understanding the complexities of migratory flows in a transnational context. In order to comprehend and tackle migration, this framework pays particular attention to investment and savings in various domains, including the financial, social, human, civic and political fields. Responding to gaps in current studies, the objective of this paper is twofold. First, it expands the asset accumulation framework by differentiating between civic and political assets. Second, using data drawn from interviews conducted among Argentine migrants and returnees in the cities of Barcelona and Buenos Aires, this paper fleshes out the definition of civic assets. The findings indicate that, for interviewees, moving to Spain implied the accumulation of civic assets that enhanced the development of a more equitable and democratic society. Respondents incorporated new civic capabilities in several areas, including increased environmental awareness and tolerance for minority groups, as well as the acquisition of knowledge about equity and labour rights. In addition, results suggest that, as a result of the migratory experience, many interviewees went through reflective processes that made them question their old presumptions about both the receiving and sending societies.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.006 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".