Bibliographic record
Abstract
Despite its importance in the global system, the literature provides little guidance on how treaty-making emerged as a well-accepted practice. In either assuming the appropriateness of treaty-making (and then analysing design) or treating treaties as strategic choices in the pursuit of gains (without analysing how treaties came to be a way to pursue gains), the current literature discounts the emergence and evolution of treaty-making. This lacuna contributes to a biased view of treaty-making as the epiphenomenal result of specific, ahistorical factors, rather than as a patterned, historical practice. We contend that the evolution of the practice of treaty-making is significant for questions of design/compliance, the future of multilateral interaction and global order. In addressing this concern, we pursue two linked goals. The first is self-consciously descriptive. We introduce a dataset of multilateral treaties that provides a novel picture of treaty-making across time, space and issue-areas. The second goal is explanatory. We develop and test a social constructivist and path-dependent explanation for the patterns of treaty-making evident in the data, especially 150 years of exponential growth, the spread of treaty-making across multiple issues and the diffusion of the practice across the world.
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 | 0.021 | 0.155 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.005 | 0.008 |
| Scholarly communication | 0.020 | 0.020 |
| Open science | 0.004 | 0.007 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.103 | 0.054 |
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".