Bibliographic record
Abstract
This article begins by critically assessing some of the current measures used to evaluate the status and impact of the Responsibility to Protect (RtoP).It then lays the groundwork for a deeper examination of RtoP's strength by specifying what kind of norm it is, and what it can reasonably be expected to do.The third section engages Zimmerman and Deitelhoff's framework on norm robustness and contestation by positing two arguments.First, the past decade of diplomatic engagement and policy development has brought about greater consensus on RtoP's core elements, and thus enhanced its validity; however, this process has also dampened many of RtoP's original cosmopolitan aspirations.Second, persistent applicatory contestation about RtoP's so-called third pillar is revealing deeper concerns about the norm's justification -thereby leading some actors to avoid framing situations with RtoP terminology.I use two cases to address the broader theoretical questions raised about whether and how language matters in assessing norm robustness: the international community's response to the deepening political violence in Burundi in 2015, and the evolution of the international community's response to the war in Syria (2011-17).While these cases illustrate changing perceptions of the political utility of RtoP language, concrete engagement by the international community, particularly in the Burundi case, indicates that RtoP's validity remains intact.The article concludes that norm decay is not equivalent to norm death, and that RtoP's prescriptions will survive given that they are embedded in a broader normative structure of human rights, humanitarian law, and civilian protection.
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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 | 0.005 | 0.051 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.038 | 0.007 |
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".