Theorizing Norm Stagnation and the Evolution of Domestic Norm Compliance
Bibliographic record
Abstract
Abstract Research on international norms is increasingly motivated by their nonlinear evolution and processes of contestation and regression. We advance this work by considering how simultaneous but distinct evolutionary shifts can create diachronic gaps between global norms and national compliance. This sequence of events in which a country that demonstrably embraces a global norm fails to keep pace as the substance of the norm evolves over time is inconsistent with recent work on international norm adaptation, resistance, and backlash. We instead theorize how the degree of norm compliance in a country can diminish over time as the broader norm evolves, a process we term “norm stagnation.” We also identify two potential sources of norm stagnation: a knowledge gap and domestic gridlock. Drawing on examples from research on the sexual orientation, gender identity, and expression (SOGIE) norm, we illustrate how both knowledge gaps and domestic gridlock can contribute to the domestic adoption of a norm becoming disjointed from the global norm as the global norm shifts over time. This theory note thus contributes to research on the dynamism of and backlash against international norms.
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 | 0.020 | 0.036 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.004 | 0.032 |
| Scholarly communication | 0.007 | 0.012 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".