Hanging Out in International Politics: Two Kinds of Explanatory Political Ethnography for IR
Bibliographic record
Abstract
The use of ethnographic methods is on the rise in International Relations. However, research in this area has largely been constrained to critical or interpretive analysis of nontraditional objects of study. This has been driven in part by two practical problems that limit ethnographic analysis: that of aggregation, as international phenomena are necessarily large in scale, and that of access, as institutional settings are often closed or secretive. While we commend critical and nontraditional research for driving much-needed expansion of the disciplinary agenda, we offer a complementary account, arguing that scholars can also use ethnographic methods in explanatory research. To do so, we draw on two methodological literatures in anthropology. The first approximates ethnographic research through historical immersion. The second applies ethnographic methods at multiple research sites, tracking transnational phenomena across them. The paper sketches prospective studies of each kind, concerning the creation and implementation of the United Nations. While neither method is entirely new to IR, the methodological literatures in question have yet to receive systematic treatment in the field.
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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 | 0.028 | 0.031 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.008 | 0.009 |
| Science and technology studies | 0.005 | 0.041 |
| Scholarly communication | 0.010 | 0.024 |
| Open science | 0.003 | 0.008 |
| Research integrity | 0.003 | 0.005 |
| 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".