Communities of Practice in World Politics: Advancing a Research Agenda
Bibliographic record
Abstract
Abstract This special forum focuses on international communities of practice (CoP) as a concept and an object of inquiry in International Relations (IR). The Introduction discusses the concept’s origins and how it has entered the field of IR. It addresses why and how CoPs matter to IR scholars by looking at how structure, agency, processes, institutions, and knowledge manifest themselves in communities. This special forum’s main contributions are: (1) capturing the political and economic effects of shared knowledge creation and diffusion through practices; (2) identifying the sources of knowledge production and learning, as well as the normative and political contestation within and between CoPs; (3) studying the social processes that originate at the boundaries between CoPs; (4) examining how people and processes generate, transform, and communicate knowledge; and (5) exploring how CoPs cultivate global governance’s norms, values, and practices from the bottom up. The Introduction also helps distinguish CoP from other relational concepts in IR such as networks, fields, regimes, and epistemic or security communities. It ends by way of situating the contributions to the special forum and outlining a research agenda on CoPs in world politics.
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 | 0.043 | 0.032 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.009 | 0.011 |
| Science and technology studies | 0.011 | 0.050 |
| Scholarly communication | 0.026 | 0.055 |
| Open science | 0.003 | 0.018 |
| Research integrity | 0.013 | 0.009 |
| Insufficient payload (model declined to judge) | 0.009 | 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".