Bibliographic record
Abstract
Landmines are a particular nasty side of war. They care nothing about your political or economic stances, age, religion, culture, social status or skin colour. Their only target is proximity. If you happen to be in the area when one goes off, there is no discrimination. It is truly a killer with no reasoning ability: just blind mayhem. This is why the elimination of this equal opportunity assassin had been the top priority of many peacemakers in the world. Jody Williams, Hendrik Ehlers, and Princess Diana have all raised awareness of this menace. However, when it comes to eliminating the more than 110 million active landmines found in over 70 countries around the world, the work becomes a little trickier. Cheap to make and plant (as low as 3 dollars per), it can cost 50 times as much to remove these hidden bombs. The question then remains, what is the best way to proceed with their eradication?
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 | 0.572 | 0.766 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.005 | 0.004 |
| Bibliometrics | 0.013 | 0.020 |
| Science and technology studies | 0.008 | 0.026 |
| Scholarly communication | 0.018 | 0.018 |
| Open science | 0.008 | 0.015 |
| Research integrity | 0.009 | 0.008 |
| Insufficient payload (model declined to judge) | 0.013 | 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".