Bibliographic record
Abstract
What is a state? This volume approaches the question from an anthropological perspective, which means that the starting point of the analysis is not the concept of the state, but instead, what kinds of structures the state consists of, what kinds of effects these structures have, and how states are experienced by the people who inhabit, make, enact, and resist them. The volume introduces a contemporary anthropological approach to the study of the state for a Finnish-speaking audience. This new approach examines the state as a diverse, socially and culturally constructed phenomenon that varies in time and place. Additional aims of the volume are to introduce and translate concepts from political anthropology to the Finnish language, and to make anthropological analyses of the state known to other disciplines that study the state and to the general Finnish-speaking public. Covering a wide variety of ethnographic contexts examining both the effects of the state and the state-like effects of other institutions, the volume contains case studies from Brazil, Uganda, Papua New Guinea, Madagascar, Finland, Bolivia, Cuba, Egypt, Fiji, Solomon Islands, and Ghana. A theoretical introduction presents the development of anthropological thinking with regard to the state and state-like institutions. An afterword reflects on the contribution of the volume in light of the ethnographic context of Indonesia.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.011 | 0.006 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.908 | 0.898 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".