Beyond Autarky: Discourses of Islandness-As-Heritage in Islands’ Energy Transitions
Bibliographic record
Abstract
This article employs heritage as a lens through which to research the roles of islandness in energy transition processes. Both in cases of islanders’ initiatives toward renewable energy projects and in cases of resistance against such projects, memories and imaginaries of islandness are evoked. The heritage of islandness is constructed discursively in response to threats and opportunities represented by the energy transition. Through an analysis of narratives in academic literature, national and local media, branding campaigns, and interview transcripts from islands in the North Sea and the Mediterranean, three common themes of islandness-as-heritage emerge across geographical difference: the island as self-sufficient ground, as laboratory of innovation, and as exploited territory. These uses of heritage are contextualized with critical counter-narratives from island studies literature, showing that the insistence on legacies of autarky, innovation, and exploitation might be contrary to the long-term interests of island communities. Instead, the activation of the heritage of interconnectedness that has historically characterized islands, islanders, and islandness, would highlight the necessary interdependence between places and could lead to an energy transition more aligned with the potentials and challenges facing the different island landscapes and their communities.
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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 | 0.004 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.009 | 0.035 |
| Scholarly communication | 0.007 | 0.010 |
| Open science | 0.001 | 0.011 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.002 | 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".