Ends and Means: Assessing the Humanitarian Impact of Commercialised Security on the Ottawa Convention Banning Anti-Personnel Mines
Bibliographic record
Abstract
The paper is divided into two parts; the first part provides an explanation and the second adds to policy-making. The first part describes the benefits arising from humanitarian demining and then identifies reasons why outside, non-state assistance is needed for this undertaking. Essential here is an understanding of the PSC, a new post-Cold War nonstate security actor. While turning to the private sector may be a necessity, the PSC industry as currently managed and regulated poses unique problems for states and NGOs in their humanitarian demining operations. In this regard, the paper explores why these problems may have been overlooked despite the negative impact of some PSC activities. Finally, the second part addresses ways to help overcome both the problems inherent in PSC activity and the reasons why solutions to them have not yet been found. It offers a sketch of an effective regulatory framework of the larger PSC industry which would have direct beneficial effects for the specific issue of humanitarian demining. Without regulation of this kind, the more problematic aspects of this new industry may gain legitimacy “through the back door” due to the current salience and popularity of humanitarian demining thanks largely due to the Ottawa Convention.
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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 | 0.004 | 0.021 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.004 | 0.010 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".