Belonging in an aquapelago: Island mobilities and emotions
Bibliographic record
Abstract
This paper concerns belonging in islands. Place-belonging conjures images of feeling at home somewhere, in our case islands. Given the emotionality of belonging, we explore island belonging through emotions. More specifically, we apply the concept of the aquapelago to island belonging and refer to this as aquapelagic belonging. Bringing in emotions, embodied perceptions and mobility, we discuss how these are assembled in island-sea relations to form aquapelagic belonging. In doing so, we draw on qualitative data from fieldwork undertaken in locations where proximity to the sea and access to seaborne mobility is paramount. Our findings demonstrate how certain emotional dispositions and mobility practices emerge in processes of aquapelagic belonging, indicating that mobility is intricately entangled with island belonging. We propose that the interconnected nature of land and sea spaces co-produce emotions of belonging in island spaces. We therefore argue that the concept of aquapelagic belonging lends useful insight to understand what is particular about island belonging. Furthermore, we suggest that attention to mobility, which in this context means navigating land/sea environments, is key to understanding aquapelagic belonging. We conclude that to grasp island belonging, the notion of the aquapelago is relevant and assists in understanding the totality of island 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 | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.006 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.000 | 0.004 |
| 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".