If Islands Did Not Exist, It Would Be Necessary to Invent Them: Grappling With Divergent Ascriptions of Islandness in Island Studies
Bibliographic record
Abstract
Island studies has had much to say about why understanding perceptions of islandness matters for understanding the world, and it has often questioned what islandness is. Island studies has nevertheless often taken for granted that islandness ought to be perceived and has often seen islandness as something good that requires preservation. The act of studying islands per se introduces the risk that a researcher will look for and look at islands without considering whether the islands they are seeing really exist to everyone, and whether they exist for certain other people in the ways the scholar believes them to exist. In this paper, we argue that there can never be a universally or objectively correct understanding of islands, as these are socially constructed geographical phenomena. Building upon the authors’ own personal engagements with questions of islandness in China, Kalaallit Nunaat/Greenland, and Fiji, we suggest it is important for scholars to think carefully about why and how islandness matters in particular places—or else to acknowledge what motivations and power relations drive them to ascribe island status.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".