Island Imaginaries: Introduction to a Special Section
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Colonial empires, scientists, philanthropists and Hollywood studios have long sustained an image of islands as remote places with unique ecologies and cultures, experimental labs, or loci of escapism. The climate crisis and the Covid-19 pandemic have contributed to a predominant view of islands as both exceptional spaces and testbeds to be scaled up onto continental or planetary levels. Likewise, the metaphor of the island is foundational to Western thought yet has been less explored in the context of scientific processes and technology development. Bringing together science and technology studies (STS) with critical Island Studies and related fields, this special section expands upon the spatial dimension of sociotechnical imaginaries to consider islands and their imaginations as both preexisting and channeling visions of science and technology. The introduced concept of Island Imaginaries captures the mutual constitution of island visions and their materialization in scientific, technological and technocratic endeavors that are imagined and pursued by scientific communities, policymakers, and other social collectives. Such an approach explores the co-constitutive dynamic of islands as sites for the foundation of technoscientific knowledge regimes, and the concomitant rendering of islands as conducive places for discovery and experimentation. The special section offers empirical case studies with insights into islands as synecdoche for larger wholes (the Earth), as experimental and exceptional sites for trialing business creation and political orders (in Singapore, and for Asia), and as variously interpreted laboratory paradise (of Hawai'i). Further research themes for STS are suggested in the Conclusion.
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Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | Science and technology studies Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Theoretical or conceptual | low |
| gpt | no category Domain: not available · Genre: Commentary About the Canadian research system: no · About a Canadian topic: no | Not applicable | high |
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 it