Argumentation and Compromise: Ireland's Selection of the Territorial Status Quo Norm
Bibliographic record
Abstract
How do states come to select norms? I contend that, given a number of conditions are present, states select norms in three ideal-typical stages: innovative argumentation, persuasive argumentation, and compromise. This norm selection mechanism departs from the existing literature in two important ways. First, my research elaborates on the literature on advocacy networks. I explain why agents engage in an advocacy for a normative idea in the first place; I add the epistemic dimension of reasoning to argumentation theory; and I show in detail the pathways through which persuasive argumentation links an advocated idea and already-established sets of meaning. Second, synthesizing rationalist and constructivist selection mechanisms, I contend that successful argumentation makes recalcitrant actors eager to reach a compromise with the advocates as long as this does not violate their most cherished beliefs. The Republic of Ireland's eventual selection of the territorial status quo norm in the late 1990s lends empirical evidence to this norm selection mechanism.
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 | 0.017 | 0.037 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.009 | 0.027 |
| Scholarly communication | 0.019 | 0.010 |
| Open science | 0.002 | 0.011 |
| Research integrity | 0.004 | 0.009 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".