Gauging the Effectiveness of Soft Law in Theory and Practice: A Case Study of the International Charter on Space and Major Disasters
Bibliographic record
Abstract
The International Charter on Space and Major Disasters is a voluntary partnership among national space agencies that provide free satellite earth observation data and information to disaster-affected States. As a nonbinding, multilateral instrument, the Charter has grown in its members, reach and application over its seventeen-year lifespan. To date, the Charter has been activated over 550 times and has provided data to 119 countries. This article provides a discussion on the current legal status of the Charter and the effectiveness of the Charter as a global governance mechanism in light of its mandate and ongoing operations. The article draws from previous reports and scholarship on the Charter, data collected through semi-structured interviews with Charter members and users, and the results from a survey distributed to Charter end users which aimed to gather information relating to the users’ experience accessing and using Charter products, as well as information relating to the extent to which the Charter contributions improved the end users’ existing disaster management capabilities. Overall the article finds that as a soft law instrument and governance tool, the Charter has been highly effective in both a legal and operational context and may provide a useful example for international cooperation in other global policy areas.
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 | 0.087 | 0.105 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.025 | 0.058 |
| Scholarly communication | 0.019 | 0.019 |
| Open science | 0.004 | 0.016 |
| Research integrity | 0.006 | 0.009 |
| Insufficient payload (model declined to judge) | 0.004 | 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".