Islandness in human rights, human rights in islandness: Missing voices
Bibliographic record
Abstract
Island Studies literature has rarely engaged with human rights law to scrutinise how the development of human rights standards and/or their (in)efficient implementation on islands contour the lives of islanders and islandness itself. Along similar lines, human rights research and practice do not systematically take into account islandness and Island Studies research. The present article explores the mutual reticence between human rights law and Island Studies, suggesting, though, that both fields can offer each other important opportunities for development. It advocates, in particular, that it is high time for a cohesive human rights and islands approach which is based on the human rights-based approach of islandness and the islandness-based approach in human rights. A potential cross-fertilisation between human rights law and Island Studies can be very promising not only for the advancement of the respective fields from a scholarly point of view, but also for a more efficient understanding of islandness and protection of human rights in practice.
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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 | 0.006 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.007 | 0.050 |
| Scholarly communication | 0.010 | 0.015 |
| Open science | 0.001 | 0.009 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.009 | 0.001 |
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".