Förbudet av Truppminor : En komparativ studie hur svenska fältarbetsreglementen har förändrats
Bibliographic record
Abstract
The prohibition of the anti-personnel landmines in 1997 have caused a great debate. The debate itself is quite unilateral, where it is mainly the supporters of the prohibition who is creating scien-tific contribution to the subject. The debate is also directed to if and why a nation should ratify the ban on antipersonnel mines, and because of that, the discussion of how a nation have or could have ratify it has been forgotten. This study examines how the Swedish counter-mobility regulations has changed after the ratification of the Ottawa-convention and the elimination of the anti-personnel mines. The purpose of this essay is to increase the knowledge how the Swedish armed forces have adapted to the ratification of the Ottawa-convention and in the long run contribute to the under-standing on how a political/humanitarian decision can affect the fighting power of a smaller nation as Sweden. Through analysis of Swedish counter-mobility regulations, it shows that some adaptations have been made, however there is still some unbalance between the methods/tactics and the available landmine equipment which results in less than maximum effect is produced with the counter-mobility actions.
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 | 0.005 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.009 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 0.002 |
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".