Conversations in and on IR: Labeling, Framing and Delimiting IR Discipline
Bibliographic record
Abstract
Calling for genuine and open dialogues between research agendas and theoretical orientations, this article seeks to put “conversations” at the center of the process of discipline-building. Just as Steve Smith declared: “We construct, and reconstruct, our disciplines just as much as we construct, and reconstruct, our world” (2004: 510), we intend to convene researchers in IR to reflect on the way we build and represent our discipline, our object of study and our community’s purposes. Applying discursive analysis and Emanuel Alder’s communitarian constructivist approach to the discipline of IR, this article will particularly discuss the use of mechanisms of labeling, cognitive structuring, and disciplinary debates to the framing of IR itself. It will propose some answers to questions such as: “What is the content and appropriate label of the discipline?”, “Who constitutes the disciplinary community?”, and “What is the legitimate purpose of the discipline?” and finally underlie some questions and contradictions in the way we understand such issues.
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 | 0.161 | 0.163 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.012 | 0.008 |
| Science and technology studies | 0.031 | 0.118 |
| Scholarly communication | 0.037 | 0.055 |
| Open science | 0.004 | 0.032 |
| Research integrity | 0.015 | 0.018 |
| Insufficient payload (model declined to judge) | 0.004 | 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".