Nondemarcated Spaces of Knowledge‐Informed Policy Making: How Useful Is the Concept of Boundary Organization in IR?
Bibliographic record
Abstract
Abstract Concepts of “boundary organization” and “boundary work,” borrowed from science and technology studies (STS), are now commonly used in International Relations to analyze organizations providing a science–policy interface. This article critically examines these concepts, with close attention to specific insights from the STS literature, for their added value in understanding the interactions between knowledge production processes on the one hand and policy making at the global level on the other. It lays the basis for two critiques: (1) an institutionalist critique of the use of these metaphors highlighting the mismatch between the interplay of relevant actors—scientists, policy makers, and stakeholders—via the social spaces they occupy and international organizations; (2) on weak assumptions on coproduction. The authors argue that the true challenge for science–policy interfaces is to generate politically “usable knowledge” and conditions for social learning, thus recognizing that politicization of science is more likely than the scientification of politics.
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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 | 0.033 | 0.037 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.005 | 0.086 |
| Scholarly communication | 0.024 | 0.037 |
| Open science | 0.004 | 0.011 |
| Research integrity | 0.007 | 0.007 |
| Insufficient payload (model declined to judge) | 0.006 | 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".