“Things of Networks”: Situating ANT in International Relations
Bibliographic record
Abstract
We come to this forum not to advocate for actor-network theory (ANT) but to raise friendly questions about its role in International Relations (IR). As outsiders to ANT, we recognize that these issues may be addressed within that broader literature. But since we are proponents of the use of frameworks that share with ANT a commitment to the analytical priority of processes, relations, and practices, we also have a particular interest in its development within our field. This forum arrives at an interesting moment for IR. Scholars working within a broadly social-constructionist framework increasingly draw upon relational and practice-theoretical approaches. Relational theories, ranging from those using the methodology of social network analysis (SNA) to post-structuralist modes of analysis, are recasting how we think about levels of analysis, actors, and the importance of social position. Practice theory revisits basic dichotomies that organize IR theory, including rationality and practicality, subjectivity and objectivity, and the ideal and the material. Relationalism and practice-theoretical accounts have demonstrated their utility in overcoming analytical problems involving, for example, the agent-structure problem and the relationship between continuity and change (Jackson and Nexon ; Hafner-Burton, Kahler, and Montgomery ; Adler and Pouliot ).
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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 | 0.011 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.007 |
| Science and technology studies | 0.009 | 0.035 |
| Scholarly communication | 0.013 | 0.024 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
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".