Bibliographic record
Abstract
In this chapter I focus on two questions that are central to a social constructivist understanding of regional orders. The first is whether and how “ideas make regions.” The second is how to conceptualize the diffusion of ideas and norms (used interchangeably here, mindful that ideas do not necessarily make behavioral claims as norms do) across the regional–global divide, that is, between regions and the global system at large, a process that is crucial to the creation and maintenance of regional orders. Until recently, international relations scholars paid scant attention to these questions. The advent of constructivism as a distinct perspective on international relations has changed that. I will argue, however, that constructivism, despite its claims to be an “ideas first” (as opposed to “ideas only”) theory, is yet to fully address these two questions. Constructivism’s position on the relationship between ideas and power remains ambiguous at best. And constructivism is especially weak when it comes to exploring the global–regional nexus in the diffusion of ideas and norms, focusing almost exclusively on how universal norms trump local or regional ones. How do ideas make regional orders? Regions as imagined communities At the outset an important question needs addressing. What does it take to be an “ideational” view of regions and regional orders? In international relations theories, the use of “ideational” has exploded with the growing popularity of constructivism. But “ideational” can mean a whole range of things, such as values, principles, ideology, culture, and identity, among others. In writings on regions, especially when it comes to defining regionness, it is not uncommon to find references to such variables as sociocultural similarity, shared values, and a common identity. And the inclusion of these elements in the literature on regions long predates constructivism.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.013 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".