Securing Networks or Networks of Security? Portuguese Emigrant Descendant Returnees Negotiate Transnational Positionings
Bibliographic record
Abstract
Abstract . The primary aim of this article is to analyze feelings of belonging and the notion of transnationality among descendants of Portuguese emigrants who have returned to Portugal. The key considerations of the analysis are the multiple interactions between place of origin and destination, transnational network constructions, and identification negotiations that often take place in the post-return. The study aims to understand how such interactions, constructions, and negotiations influence the behavior of these returnees and their life options, and also to under-stand the way that identities and senses of belonging are (re)molded once in Portugal. This article thus sets out to answer two overarching questions: (1) what are the impacts of return upon Portuguese emigrant descendant returnees and where does the return itself leave the returnees in the transnational stratum, and in their search for the “self”?; (2) how are social networks (re)negotiated in the post-return taking into consideration tactics of insertion and returnees’ transnational positioning? In order to reach conclusions, the findings presented in this article rely on interviews with 40 Portuguese emigrant descendant returnees from two source countries: Canada and France. This ethnographic fieldwork was carried out between 2008 and 2010, with a second round of follow-up interviews carried out between 2011 and 2013.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.005 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.000 | 0.003 |
| 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".