Others, the Nation, and its (Dis)integration in ‘European’ Lithuania
Bibliographic record
Abstract
As in other member states of the European Union, in post-socialist Lithuania lofty ideals of pan-continental ‘integration’ are countered by local understandings of what constitutes belonging and togetherness. Rather than unquestioningly embracing the idea of the European supranation, many Lithuanians reassert their national selves and adopt an increasingly critical stance towards grand projects intended to create continental oneness. What politicians in Vilnius and bureaucrats in Brussels prescribe as integration many see as a threat to the integrity of the nation. In daily social life, for many this perceived threat is embodied by various ethnic ‘others’ who have recently settled in Lithuania as mostly economic migrants from so-called third countries. Using interviews and materials from print and electronic media collected in 2008–2009, this article examines popular perceptions of such ‘others’. It is proposed that heightened awareness of, and defensive (or offensive) dispositions toward, persons conceptualized as ‘other’ should be investigated at the intersection with the EU's agendas of supranational integration. The article further suggests that in ‘European’ Lithuania predominantly negative stances vis-à-vis people thought of as alien to the nation can be viewed as a response to broader processes of unsettling change brought about by EU expansionism and its policies promoting pan-continental unity.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.009 | 0.018 |
| Scholarly communication | 0.007 | 0.003 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.001 |
| 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".