Bibliographic record
Abstract
This study will focus on the challenges that international law must face to identify and rebuild states that are considered as failed. How can failed states be restored to the status of successful ones, which is a misguided approach when it is known that they never had success? For international law, indicators should be available to identify failures and to identify states threatened by them. The UN has acquired a certain experience in this matter but lacks proper means for dealing with such challenges. It was able to define integrated strategies of nation- building and state-building, with the help of regional organizations, specialized institutions, NGOs, and the states concerned. This essay raises the question of the position of international law regarding the existence of this concept. It adopts an analysis that assesses the legal implications of failed states, for the state itself and for the international community as well. As a result, the concept of failed states is not accepted by all scholars in international law. For some authors it is inappropriate, harmful; it is damaging for the image of the state as a member of international community which is composed of a territory, population, and a political authority and enjoys sovereignty. To avoid legal implications, some scholars argue that it is better to use other terms, such as ‘fragile state’, ‘state in crisis’ and ‘failed government’ to designate this highly important subject in international law.
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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 | 0.008 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.008 | 0.091 |
| Scholarly communication | 0.011 | 0.018 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.007 | 0.008 |
| 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".