Taking Boundary Work Seriously: Towards a Systemic Approach to the Analysis of Interactions Between Knowledge Production and Decision-Making on Sustainable Development
Bibliographic record
Abstract
The concept of boundary work has been put forward as an analytical approach towards the study of interactions between science and policy. While the concept has been useful as a case-study approach, there are several weaknesses and constraints when using the concept in a more systemic analysis of the interactions between knowledge production and sustainable development decision-making at the international level, such as its inability to capture the diversity of institutions involved in such boundary work. Another inability involves a lack of conceptualisation of the impacts of the specific conditions of intergovernmental decision-making, such as rules for representation and the mode of negotiation. This chapter suggests complementing the concept of boundary work with a configuration approach based on a two-dimensional conceptualisation of the boundary space in international decision-making that allows the positioning of institutions with regard to their degree of politicisation and their position in terms of national and regional representation. Such an approach could be a useful guide in the further conceptualisation and application of the boundary concept. These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
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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 | 0.005 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.004 | 0.030 |
| Scholarly communication | 0.013 | 0.017 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.004 | 0.006 |
| 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".