Why a Book on Danish Politics?
Bibliographic record
Abstract
Danish politics is comparable to the politics of other small European states. However, it is also unique because of its many years of minority governments, its history of EU opt-outs, its high electoral turnout even in local elections, and its high level of trust in government and Parliament. Other remarkable features are high female labour participation in spite of a lack of proactive gender policies, and one of the world’s largest local and regional government sectors. Denmark had its earthquake election as early as 1973 with many new parties entering Parliament. However, the June 2019 elections still saw the huge majority of voters voting for old parties. Denmark is also known as a country with a high taxation level and one of the world’s biggest publicly funded service sectors, possibly because minority governments strive for majority support for their legislative proposals. Other specific characteristics are the mix of market-oriented policies and the huge welfare state. These topics—and many more—are presented, analysed, and discussed in the book. The intention has been that the chapters should reflect the state-of-the-art in research on the various topics and simultaneously provide new knowledge and suggest future lines of research.
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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.008 | 0.004 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.038 | 0.015 |
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".