An Ethnographic Study of the State in Rural Solomon Islands (Lau, North Malaita): A Quest for Autonomy in Global Dependencies
Bibliographic record
Abstract
This thesis is based on ethnographic fieldwork in Solomon Islands capital, Honiara (four months), and in the rural Lau Lagoon, Malaita Province (eight months). It examines how the Solomon Islands state, marked by a recent history of civil conflict and foreign military intervention, becomes visible in the everyday lives of rural and, to a lesser degree, urban non-elites; and how this visibility affects non-elite perceptions of the state as legitimate, dominant governing system. Non-elites are defined as those Solomon Islanders who often have only completed some primary/secondary education, whose first and primary language is their vernacular, and whose affluence is defined by their reliance on slash-and-burn agriculture, non-industrial fishing and micro-economic activities. Theoretically, this thesis draws on literature that defines the state through its ability to become invisible in everyday routines in such a way that state legitimacy and dominance as governing system are rarely questioned, and if they are only during temporary disruptions. \n \nI propose that to understand to what extent and how the Solomon Islands state is integrated into everyday routines it is necessary to focus on mundane encounters with the state, its infrastructures and representatives as well as available alternatives; and to do so by prioritizing the perspective of the non-elites rather than the perspective of the disciplining state and state-focused members of (an urban) civil society. The Solomon Islands case is ideally suited for such an analysis because in this historically, linguistically and culturally diverse country the centralized state has been found to continuously struggle with diverging local conceptualizations of government and governance. My findings highlight that the Solomon Islands state has failed to become integrated into daily routines. Instead it is nearly continuously visible as a disruptive force. As a result non-elites continue to defy state-based unification and instead seek relative autonomy from the state by emphasizing the dominance and legitimacy of village-centric governance. This quest for autonomy is, however, increasingly curtailed by dependency on foreign foods and goods, and therein by a dependency on the state as primary globally-recognized legitimate mediator of economic relations.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.007 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".