Hierarchy in Regime Complexes: Understanding Authority in Antarctic Governance
Bibliographic record
Abstract
Abstract Many scholars argue that regime complexes are nonhierarchical. However, if that is true, then how does authority function? This article argues that the conceptualization of regime complexes as largely devoid of hierarchy is mistaken. Instead, it offers a new definition of regime complexes: emergent patterns of authority among state and non-state actors, which vary in their degree of hierarchy. Hierarchy in regime complexes looks different from political scientists’ traditional conceptualization. It is systemic, emergent, and positional. I present two dimensions of variation in hierarchy: deference and autonomy. These dimensions provide both a conceptual and an empirical strategy for understanding how authority relations are constituted. Conceptually, they allow us to “see” hierarchy in regime complexes. Empirically, they provide transparent, replicable and variable measures, which have eluded much of the work to date. I use topic modeling coupled with network analysis to detect hierarchy in the regime complex for Antarctica. I demonstrate that the inclusion of non-state actors and their governance activities changes our understanding of the Antarctic regime complex. This approach reveals a hierarchical regime complex, where some non-state actors have considerable authority and are governing issues not regulated by formal rules.
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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 | 0.003 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.002 | 0.009 |
| Scholarly communication | 0.006 | 0.007 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| 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".