A practice in search of a theory
Bibliographic record
Abstract
‘The grading of powers is a matter of theory’, Martin Wight once remarked, whereas the ‘managerial function of the great powers is a matter of practice’. Special responsibilities erode this distinction: they are about the grading of powers, as well as their managerial functions. Accordingly, this chapter traces how special responsibilities came to be developed in both theory and practice. At its heart is to be found the complex interplay between the category deemed to be special, and the nature of the responsibility that is as a result attached to it. Special responsibilities first arose within a traditional and European-centred states system. In that context, it was the seemingly fixed category of the special that attracted attention; subsequently, a more reflexive understanding of responsibility has led to a more fluid understanding of those who are to be considered special, and for what specific purpose. This fluidity has tracked the more complex global system that has evolved in the meantime. This chapter clearly demonstrates that there was a practice of special responsibilities long before there developed any coherent ‘theory’ to account for it. When a theory began to emerge, it was one that largely emphasised a responsibility attaching to a pre-existing group of states, namely the great powers. That the explanation and justification took this particular form, rather than something else, meant there were a number of significant consequences. Any attempt to make sense of special responsibilities must therefore begin with a brief history of its practice, and how this subsequently came to be engaged in the International Relations (IR) theory literature.
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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 | 0.006 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.037 |
| Scholarly communication | 0.009 | 0.011 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.004 | 0.008 |
| Insufficient payload (model declined to judge) | 0.012 | 0.005 |
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".