If ethnography is more than participant-observation, then relations are more than connections: The case for nonlocal ethnography in a world of apparatuses
Bibliographic record
Abstract
Efforts to theorize globalization remain limited by an ethnographic data set obtained primarily through direct sensory experience. This article argues that such empiricism persists because the difference between connections and relations as methodological constructs remains blurred. Their conflation precludes a fuller view of how apparatuses organize global processes. Apparatuses decompose direct social connections and replace them with shifting constellations of indirect social relations. Unlike connections, relations are mediated by abstract third agents and have an arbitrary relationship in/to space and time. This weakens participant-observation’s ability to capture an apparatus’s operations. As a remedy, the article suggests ‘nonlocal’ ethnography, which examines how disconnected actors utilize an apparatus’s mediating agents – e.g. statistical calculations, probabilities estimates, high-scale moral narratives, and interpretative paradigms – to channel the global circulation of migrants. The argument for the apparatus’s theoretical value and nonlocal ethnography’s methodological value is illustrated through an illegal migration journey from Senegal to Italy.
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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 | 0.056 | 0.050 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.006 | 0.069 |
| Scholarly communication | 0.011 | 0.030 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.005 | 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".