Conscription in Scandinavia During the Last Quarter Century: Developments and Arguments
Bibliographic record
Abstract
This article describes the empirical changes of conscription in Scandinavia since 1970 from four perspectives, i.e., the demography, economy, organization, and personnel that give Denmark, Sweden, and Norway a quite different conscription profile. No easy explanations are given for these changes which are products of complex and sometimes even contradictory decisions made at the national and organizational levels. Instead, the present conscription profiles of Denmark, and Sweden, and Norway are identified as one of three ideal reasons for using conscripts: Democracy, Deterrence, and Deployment abroad, the DDD-model. This theoretical model suggests both national and international reasons for the continuation of conscription and thus argues against reducing or abolishing conscription due to the end of the Cold War and relying, instead, on a professional army. In addition, conscripts may be better qualified for international peacekeeping missions than regulars because they can more easily identify with the local civil population. The announcement of the end of conscription as a result of the end of the Cold War, therefore, seems somewhat premature.
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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 | 0.004 | 0.006 |
| Science and technology studies | 0.003 | 0.005 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 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".