Scenarios and Ethnography: Infrastructural Futures as Windows into the Present
Bibliographic record
Abstract
Large-scale infrastructures are typically part of development projects that are global in ambition and local in their impacts. While anthropology has a decent track record of using ethnographic methods in the study of infrastructure, it typically lacks the capacity to provoke statements or attitudes regarding larger development plans. Scenario workshops, initially developed by researchers in the field of foresight studies, turn out to be productive tools in eliciting assessments of the present by talking about possible futures. The European Research Council project InfraNorth conducted scenario workshops in two locations in Canada and Norway in 2023, in which four scenarios were presented and discussed. Apart from speculations about what the future might bring, these discussions provided ethnographic insights that went beyond what we had found before through more traditional means of ethnography. We suggest that scenarios and scenario workshops have the potential to offer ethnographic windows into infrastructural presents by talking about the future.
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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 | 0.014 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.005 | 0.026 |
| Scholarly communication | 0.007 | 0.021 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".