Knowledge in power: the epistemic construction of global governance
Bibliographic record
Abstract
This chapter aims to anchor a normative theory of global governance in a reworked conception of epistemes that accounts for the role of productive power and institutional power in setting the conditions of possibility for good (moral) global governance. We outline our argument in three parts. First, we reintroduce a modified conception of episteme into the international relations (IR) literature to argue that power is a disposition (in the sense of ordering or controlling) that depends on knowledge. Power is also productive in the sense of defining the order of global things, to paraphrase Michel Foucault. In addition, we try to show that power's productive capacity is often followed by the development of formal and informal institutions that play a role in fixing meanings, which are necessary for global governance. Second, we put forward a normative theory of the requirements of global governance that builds on these notions. We argue that global governance rests on material capabilities and knowledge, without which there is no governance, and legitimacy and fairness, without which there is no moral governance. Third, we bring these insights to bear on a brief discussion of the effects of epistemes on emerging pockets of global governance and the possibilities and limits of moving global governance in a more sustainable and just direction. We use international trade and the related legal system to illustrate the above relationship.
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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 | 0.004 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.032 |
| Scholarly communication | 0.008 | 0.014 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| 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".