Bibliographic record
Abstract
Abstract Best practices are increasingly used to govern a range of global issues. Yet, the rise of global governance through best practices has received scant attention in the International Relations literature. How do best practices differ from other modes of governance? How are they constructed? And to what end? We offer a novel conceptualisation of best practices as a unique mode of global governance principally distinguished by basing claims of political authority on existing practices. Belying their apolitical terminology, best practices in global governance are purposively constructed by political actors to steer targeted actors toward desired ends. We illustrate the characteristics of governance through best practices with reference to state and non-state global governance initiatives in a wide range of issue areas, ranging from finance and development to human rights and the environment, and through an in-depth case study of the ISEAL Alliance, a disseminator of best practices for transnational sustainability standard-setters. We find that governance through best practices has both positive and negative consequences. While it offers a pragmatic approach to global governance under conditions of fragmentation and polycentricity, it can also mask underlying power dynamics and political agendas and therefore requires ongoing critical scrutiny.
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 | 0.044 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.010 |
| Science and technology studies | 0.005 | 0.061 |
| Scholarly communication | 0.019 | 0.015 |
| Open science | 0.003 | 0.009 |
| Research integrity | 0.006 | 0.007 |
| Insufficient payload (model declined to judge) | 0.003 | 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".