Floating Islands, Frontiers, and Other Boundary Objects on the Edge of Oceania’s Futurity
Bibliographic record
Abstract
Technological advances have challenged numerous social and political domains over recent decades, including the materialities and imaginaries of islands and islandness in Oceania. Since the early 2000s, a plurality of schemes, discourses, politics, anxieties, and hopes have coalesced around the possible construction of artificial islands, referred to as floating islands, floating nations, floating cities, or seasteads, depending on the new islands’ imagined purposes and peoples. If achieved, these new, de novo , islands will contribute to an ongoing regional geopolitical remaking that requires urgent attention. However, in examining floating islands as boundary objects, this article suggests that, even if never realized, they are exceptional points of focus for perceiving and reflecting on the uncanny, disruptive character of capital at work in the contemporary Pacific Islands in tension with multi-state regional policy initiatives for collective governance and sustainable ocean management. Moreover, this article argues that floating islands are not the only “artificial islands” producing tensions between communities, states, and international ocean governance frameworks. Deep-sea concessions for mineral exploitation, the spatialization of high-seas fishing rights, and large- and small-scale conservation zones similarly raise issues of the fixity or fluidity of territoriality, sovereignty, rights of access and restriction to common or uncommon marine spaces and their resources, as well as conflicting imaginaries and ideologies around the ocean and Oceania as an open frontier.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.009 | 0.043 |
| Scholarly communication | 0.010 | 0.011 |
| Open science | 0.001 | 0.012 |
| Research integrity | 0.002 | 0.003 |
| 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".