Bibliographic record
Abstract
As the Baltic states commemorated the centenary of their first appearance as independent states in 2018, their celebrations were mixed with feelings of ambiguity about the road travelled since then. Although today we often see Estonia, Latvia, and Lithuania as 'post-communist' countries, their experience with communism was actually much harsher than in Central Europe, since, for nearly fifty years, the three countries were forcibly a part of the Soviet Union. This has made their journey back into the European community all that more remarkable, and it has also served to keep these countries somewhat more resistant to the dangers of democratic backsliding. After all, their continued independence and well-being are intricately dependent on keeping the European liberal order intact. Nevertheless, the winds of populism have also begun to buffet these three countries, meaning that they have been struggling to keep their balancing act going. This article reviews the development of the Baltic states over the last 20 years, both in terms of domestic politics and EU accession and membership. It profiles the way in which the three countries have been trying to keep their faith in democracy and liberalism alive amidst ever more turbulent political and economic times.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 |
| 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 | 0.011 |
| Scholarly communication | 0.011 | 0.004 |
| Open science | 0.000 | 0.007 |
| Research integrity | 0.002 | 0.003 |
| 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".